(408,95,74,NULL,NULL,'74_95_4609098526699557b64ee4_Gowtham_Mohanraj_HMI_Developer_Resume_1_(1).pdf','Gowtham    Mohanraj HMI Developer Resume 1 (1).pdf','ED U C ATIO N\nG OW TH AM  SA SSIS TA N T M AN AG ER	 	C O NTA C T+91 6 383311446go w tth am s6 @ gm ail.c o m ch en naiS K IL LS	Pro je ct M an ag e m en t	 	\nT eam work\nT im e M an ag e m en t\nLe ad ers h ip\nE ffe ctiv e  C om mun ic a tio n\nC rit ic a l  T hin kin g	\nV ELA LA R C O LLE G E O F\nEN GIN EER IN G A N D T EC H NO LO GY\nEle ctr o nic s &amp;  C om mun ic a tio n -  7 5%2 010  -  2 01 32009PO NNU M ATR IC U LA TIO N H R S E C\nSC H O OL\nHig h er s e co nd ary  e d uca tio n -  8 5%W ORK  E X PER IE N CEPR O JE C TS	 	P R O FIL EDyn am ic   an d  in no vativ e   in fo ta in m en t  sy ste m  exp ert  w it h  o ve r  4  ye ars   o f\nexp erie n ce   in   deve lo pin g  an d   im ple m en tin g  cu ttin g-e d ge   in   au to m otiv e\nin d ustr y  a n d  in fo rm atio n s y ste m s.  O ve r a ll  1 0 y e ars  o f e xp erie n ce  in  S o ft w are\nin d ustr y .  P ro ve n  a b ili t y  to  le ad  c ro ss-fu n ctio nal  te am s  a n d  m an ag e  p ro je cts\nfr o m  co nce p t  to   co m ple tio n,  en su rin g  se am le ss  in te gra tio n  of  m ult im ed ia ,\nn av ig atio n,  an d   co nnectiv it y   fe atu re s.  Adep t  at  le ve ra g in g  ad van ce d\nte ch no lo gie s t o  e n han ce  u se r e xp erie n ce  a n d  d riv e  m ark e t c o m petit iv e n ess.\n2 023 -  P R ESE N TM on tr a  - T I c le an  m ob il it y  p riv ate  l im it e dA ssis ta n t m an ag er	 	\nP ro d uct d eve lo p m en t &amp;  R eq uir e m en ts  h an d lin g	  -  C en tr a l R &amp; D	The  cre atio n   an d   exe cu tio n   of  m an ual  te st  ca se s  fo r  all  m od ule s  an d\nap plic a tio n s o f v e h ic le  t e le m atic s.\nT he c re atio n  o f  v e h ic le  a le rts  a n d  in fo rm atio n  th ro u g h th e te le m atic s c lo u d\nfo r m ob il e  a p plic a tio n s a n d  O EM .\nFu n ctio n al  a n d  in te g ra tio n  te stin g  o f  a p plic a tio n  fe atu re s a t  b oth  th e te st\nb en ch  a n d  v e h ic le  l e ve ls  u sin g  a  C AN  A naly ze r.\nC usto m iz in g  th e te le m atic s d ash b oard  a n d  m an ag in g  it   a cco rd in g  to  re al-\nt im e f ie ld  i s su es a n d  s e rv ic e  r e q uir e m en ts .\nD eve lo p m en t  te stin g  o f  v e h ic le  fe atu re s s u ch  a s g eo fe n cin g ,  liv e  tr a ckin g ,\nan d  v e h ic le  a le rts  a n d  A nd ro id  d eve lo p m en t i n  i n fo ta in m en t s y ste m .\nTele m atic s p erfo rm an ce  a n aly sis  t h ro u g h v a rio u s c o m petit iv e  b en ch m ark in g .\nSu p port t h e v e n d or s e le ctio n  p ro ce ss a n d  m an ag e v e n d ors  t o  e n su re  t im ely\nd eliv e ry  a n d  t h e r e q uir e d  q ualit y .\nG ood  k n ow le d ge a n d  u n d ers ta n d in g  o f  E V  a rc h it e ctu re  w it h  m ult ip le  C AN\nnetw ork s i n  t h e v e h ic le .\nP re p ara tio n   of  8D  re p orts   in   co ord in atio n   w it h   pro d uctio n   an d   cro ss-\nfu n ctio n al t e am s t o  s o lv e  c rit ic a l q ualit y  i s su es.\nE xp ertis e  i n  p re p arin g  S co p e o f R eq uir e m en ts  d ocu m en ts .\nT eam  s iz e  :  2\nI n it ia tin g  a n d  im ple m en tin g  U D S p ro to co l  a cro ss  a ll  ve h ic le  d ow nstr e am\nE C Us, c o n sid erin g  a ll a sp ects  a n d  v e rtic e s.\nS u p portin g  t h e d eve lo p m en t o f a  s in g le  c lo u d  p la tfo rm  f o r v a rio u s t e le m atic s\nu n it s .\nA ctiv e ly  in vo lv e d  in  t h e im ple m en ta tio n  a n d  v a lid atio n  o f	  t e le m atic s f e atu re s	\nsu ch  a s :\n D eep  l o g ger -	  V erif ic a tio n  a n d  v a lid atio n  o f e ach  v e h ic le  d ata  p oin t.	1.\n D eep  u p d ate r -	  D eve lo p m en t o f U D S p ro to co l i n  a ll E C Us t o  s u p port	\nF ir m ware  O ve r-T he-A ir  	(F O TA	) u p d ate s.	\n2.\nD eep  c o m man d er -  D eve lo p m en t o f v e h ic le  I m m ob il iz a tio n  s y ste m s.	3.	\nT O OLS	 	CA N oe\nC A N an aly ze r\nB us M aste r\nP -C A N\nJT A G\n\nDoco m o I n te rto u ch  I n d ia  L td  ,  B an g alo re , I n d ia	 	IT  S u p port E n g in eer	M S O utlo ok c o n fig ura tio n , b acku p  ,  r e sto re  a n d  t r o u b le sh ootin g .\nM an ag e D om ain  e n vir o n m en t f o r t w o s it e s t h ro u g h r e m ote  s u p port.\nW ir e le ss n etw ork in g  i n  D om ain  e n vir o n m en t.\nA sse m blin g , h ard w are  u p  g ra d atio n  a n d  t r o u b le sh ootin g .\nPro vid in g  s u p port t o  i n sta llin g  O S f o r t h e u se r m ach in es a n d  s e rv e rs .	\n G em alt o  M id dle  E ast F Z  L LC , D ub ai, U .A .E	                              	2 016 - 2 020	 	 2013- 2 016	 	IS S/N etw ork  a n d  S u p port E n g in eer	R esp on sib le   fo r	   	LA N  an d  W AN  n etw ork   op era tio n s,  sy ste m  d is a ste r	\nre co ve ry ,  data b ase   an d   sy ste m  backu p ,  Pla ye d   ke y  ro le   in   backu p\nm an ag em en t o f I T  d ep artm en t.\nC IS C O   :  Rou te rs ,  Sw it c h es,  Serv e r,  wir e le ss  devic e s,  an d   Acce ss\nte ch nolo g ie s.\nS tr o n g  t r o u b le sh ootin g  s k il ls  o n  L A N , W AN  a n d  W ir e le ss t e ch nolo g ie s.\nM an ag ed  u se r a cco u n ts  w it h  ( A D ) A ctiv e  D ir e cto ry  f o r t h e u se rs  t o  p ro vid e\ncre atio n ,  d ele tio n ,  u se r p erm is sio n  a n d  m an ag ed  u se r s e cu rit y  w it h  g ro u p\np olic ie s\nG ood  e xp erie n ce  i n  i n sta llin g  a n d  m ain ta in in g  t h e v ir tu al w ork sta tio n s	\n        	( E 2C )\nH an d lin g  P K I a n d  V PN  b ase d  s e cu rit y  i s su es.  p erfo rm  t r o u b le sh ootin g  e n d -\nto -e n d  b etw een  t h e s it e s.  P ro vid in g  t r a in in g  fo r t h e u se rs  t o  c o n fig ure  t h e\nce rtif ic a te  a n d  s e cu re  d om ain  w it h  t w o f a cto r a u th en tic a tio n .\nP erfo rm in g   Reg is tr a tio n   an d   Auth orit y   dutie s  to   in clu d e  ce rtif ic a te\ne n ro lm en t a n d  r e vo ca tio n .\nM an ag ed  A cce ss C on tr o l  S yste m  to  m ain ta in  a ll  th e b ad ges o f  u se rs  a n d\nvis it o rs .  T o p ro vid e p erm is sio n s fo r  th e re co m men d ed  d ep ortm en t  a n d\nse cu re d  a re a.\nIn sta llin g  G em alt o  B om gar  a p plic a tio n  fo r  a ll  th e m ach in es in  p ro d uctio n\nare a.	\nK in g sto n  I n fo  s o lu tio n s,B an g alo re .	                                       	2 020 -	  2 023	            	\n        \n       	 T est E n g in eer	\n 	\nperfo rm in g  v a lid atio n  o n  i n fo ta in m en t f e atu re s i n  a ll p ossib le  c o n d it io n s.\nV alid atin g  d if fe re n t m od ule s l ik e  U SB ,F M ,H M I a n d  P hon e	 	\nP erfo rm in g  th e ra p id  v a lid atio n  te sts   fo r  a ll  te st  c o n d it io n s  a n d  in va lid\nc a se s	 	\nP re p arin g  s o ft w are  v a lid atio n  s ta tu s r e p ort .\nD eve lo p m en t a n d  v a lid atio n  o f i n te g ra te d  T ele m atic s a n d  C lu ste r.	\n 	\nI h ave  a ctiv e ly  in it ia te d  a n d  d eve lo p ed  F O TA  im ple m en ta tio n  in  3 W  ( L 5 M ,\nL5 N ),  T ra cto r,  S C V,  a n d  H CV.  S u cce ssfu lly ,  I  h ave  g iv e n  a  d em o w it h  t w o\nve rtic e s.	 	\nI p ro p ose d  F O TA  e n tr y -le ve l c o n d it io n s a n d  c h ecklis ts  b ase d  o n  i n d iv id ual\nE C U a n d  v e h ic le  b eh avio u r,  e n su rin g  n o im pact o n  t h e e n d  c u sto m er a n d\nm ain ta in in g  v e h ic le  p erfo rm an ce .	\nL A N G UAG ES	Engli s h	 	\nIE LT S w it h  b an d  6\nT am il	 	\nS TA C K H O LD ER S	Sib ro s ,  N esh  ,  C onnectm  ,\nP ric o l  ,  E le ctr o nik a ,  A W EV ,\nSo na C om sta r ,E xid e	 	\n      \nO TH ER  I N FO	Passp o rt n o  - K 2849988	 	\nH obbie s - S ke tc h in g &amp;\nG ard en in g	 	\nT han ks	 	G O W TH AM  S A KTH IV EL	,  C hen nai,  2 4 J u n e 2 024',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-18 17:48:43',0),(409,96,73,44,NULL,'73_96_9514763636699eb21f28a7_Develoeper-_Connected_Vehicle-Avinash_Thakur.pdf','Develoeper- Connected Vehicle-Avinash Thakur.pdf','A vin ash	\nT haku r	\nA nd ro id	\nF ra m ew ork	\nD eve lo per	\nB ach e lo r	\no f	\nE ng in e erin g	\ni n	\nE C E	\n-\n2 0 16	\n7 4 118 79 73 8\nt h aku ra vi2 19 1@ gm ail.c o m\n\nP R O FE S SIO NAL	\nP R O FIL E	\n\nE xp erie nce d	\nA nd ro id	\nf r a m ew ork	\ne ng in e er	\nw it h	\no ve r	\n6	\ny e ars	\ni n	\nA uto m otiv e	\nI n fo ta in m ent,	\nI V I	\ns y ste m s,	\nA O SP	\nP ortin g ,	\na n d	\nC onsu m er	\nE le ctr o nic s.	\n\nT E C H NIC A L	\nE X PER TIS E	\n\n●	\nA nd ro id	\nA uto m otiv e :	\nC ar	\nA PI,	\nC arS erv ic e ,	\nS yste m	\nS ta te	\nM an ag em ent,	\nO TA .	\n●	\nH yp erv is o r:	\nX en	\nH yp erv is o r.	\n●	\nD eve lo p m en t	\nT o ols :	\nA nd ro id	\nS tu d io ,	\na d b,	\nP utty ,	\nG it ,	\nC lo ne zilla	\n●	\nL a n g uag es:	\nJ a va ,	\nC ++,	\nC ,	\nR x-J a va	\n●	\nO S:	\nA O SP,	\nU buntu ,	\nW in d ow s,	\nL in ux	\n●	\nP ro to co ls :	\nO BD -II,	\nJ 19 39	\n●	\nV ers io n	\nC on tr o l:	\nG it ,	\nS ub ve rs io n,	\nG it h ub ,	\nB it b uck e t	\n●	\nC on ﬁ g ura tio n	\nM an ag em en t:	\nJ ir a ,	\nJ e nkin s,	\nC onﬂu e nce	\n\nP R O JE C TS	\nE X PER IE N CE	\n\nL u xo ft	\n@	\nA M D,	\nB en g alu ru	\n&ndash;	\nS enio r	\nS oft w are	\nE ng in e er	\n2 nd	\nM ay	\n2 0 23	\n&ndash;	\na t	\np re se nt	\n●	\nB oard	\nb rin g -u p	\nf o r	\nA AO SP	\na s	\nD UAN	\no n	\nt h e	\nU buntu /Y o cto	\np la tfo rm ,	\nu sin g	\na	\nh yp erv is o r.	\n●	\nS etu p	\na n d	\nc o nﬁg ura tio n	\no f	\nh yp erv is o r	\nf o r	\nd is p la y,	\nU SB ,	\na n d	\nd eb ug	\ns e tu p .	\n●	\nC usto m iz e d	\nA AO SP	\nf o r	\nc lie nt	\nn e ed s.	\n●	\nD eb ug ged	\na n d	\nﬁ xe d	\nA AO SP	\ni s su e s.	\n\nL u xo ft	\n@	\nF o rd	\nM oto rs ,	\nB en g alu ru	\n&ndash;	\nS oft w are	\nE ng in e er	\n9 th	\nJ u ly	\n2 0 21	\n&ndash;	\n1\ns t\nM ay	\n2 0 23	\n●	\nA O SP	\np la tfo rm	\nd eve lo pm ent	\na n d	\ne nh an ce m ent.	\n●	\nI m pro ve	\nt h e	\ns ta rtu p	\nt im e	\ni n	\nt h e	\nO S	\nl a ye r	\nw it h	\nl e ss	\nf u tu re	\nm ain te nan ce .	\n●	\nA TS ,	\nC TS	\ni s su e s	\nd eb ug gin g ,	\na n d	\nb ug	\nﬁ xin g\n\nH arm an	\nC on necte d	\nS erv ic e ,	\nB en g alu ru	\n&ndash;	\nS oft w are	\nE ng in e er-P ro d uct	\nD eve lo pm ent\n1 1th	\nN ov	\n2 0 19	\n&ndash;	\n5 th	\nJ u ly	\n2 0 21	\n●	\nM an ag ed	\nN AD	\ns ta te ,	\nC PU	\nu sa g e,	\na n d	\nm em ory	\no ptim iz a tio ns.	\n●	\nC ond ucte d	\nF O TA	\nu p d ate s	\nu sin g	\nR ed	\nB end .	\n●	\nW ork e d	\no n	\nA D AS	\nF FC ,	\nC FC	\ns e ttin g s,	\na n d	\ns y ste m	\na p p	\nc o m patib ilit y .	\n\nS ask e n	\nT e ch nolo g ie s,	\nB en g alu ru	\n-\nE N G IN EER	\n-\nA PP	\nS O FT W ARE	\nD ES IG N	\n&amp;	\n\nD EV ELO PM EN T\n1 0 th	\nJ a n	\n2 0 19	\n&ndash;	\n8\nt h	\nN ov	\n2 0 19	\n●	\nI m ple m ente d	\nR eal- T im e	\nT e xt	\nf o r	\nO re o .	\n●	\nC re ate d	\nc u sto m	\ns y ste m	\ns e rv ic e s	\nf o r	\nA nd ro id	\nO re o .	\nN atio n al	\nI n fo rm atic s	\nC en te r,	\nB en g alu ru	\n-\nA nd ro id	\nd eve lo per	\n1 s t	\nF e b	\n2 0 18	\n-\n7 th	\nJ a n	\n2 0 19	\n●	\nD eve lo ped	\na	\nR atio n	\nD is tr ib utio n	\nA pp	\nw it h	\nb io m etr ic	\na u th e ntic a tio n.	\nA gile	\nH ealt h	\nT e ch nolo g ie s,	\nB en g alu ru	\n-\nA nd ro id	\nd eve lo per	\n1 s t	\nA pril	\n2 0 17	\n-\n2 nd	\nJ a n	\n2 0 18	\n●	\nD eve lo ped	\nD r.S ah e li	\nA pp	\na n d	\nA H TN ET	\nT im esh e et	\na p plic a tio n.	\nE D U CATIO N\nN IE	\nI n stit u te	\no f	\nE ng in eerin g ,	\nM yso re	\n-\nB ach e lo r	\no f	\nE ng in e erin g	\ni n	\nE C E	\nS EP TEM BER	\n2 0 12	\n-\nA U G UST	\n2 0 16	\nS co re :	\n5 6 %	\nL .C	\nC olle g e,	\nB ih ar	\n-\nP U C	\nY EA R	\n2 0 11	\nS co re :	\n6 1%	\nS T	\nM ary &rsquo;s	\nH ig h	\nS ch ool,	\nJ h ark h an d	\n-\nS C H O OL	\nY EA R	\n2 0 08	\nS co re :	\n7 2 %',2,2,1,'Recruiter','SL','N',14,1,'2024-07-22 05:49:49','2024-07-19 05:49:49',0,NULL,NULL,'2024-07-19 04:27:13',0),(410,97,73,35,NULL,'73_97_3304958496699f09ec2ab7_Bhushan_Bende_Developer_Connected_Platform_(1).pdf','Bhushan Bende_Developer_Connected Platform (1).pdf','Bhushan	Bende	\nSoftware	Engineer	\nRoles	:Senior	Software	Engineer	\nE-mail:	bhushanyahoo@gmail.com	\nMobile:	9405708908	\nProfessional	Synopsis:	\n	Experience	inPython	Developer	,Building	APIand	Data	Pipeline	,with	PRDomain	\n	Experience	onworking/building	Python-Fast	API	hosted	onLambda	connected	with	Elastic	Search	\n	Building	ETL	&amp;ELT	Data	Pipeline	inPython	with	AWS	Managed	Streaming	Application	Flink,	Self	Managed	Flink	and	AWS	Lambda	\n	5years	ofFunctional	and	Automation	Testing	experience	inInvestment	Banking	Domain	,Payment	Domain	and	Media	Monitoring.	\n	Keen	understanding	onCapital	Market	and	Money	Market.	\n	Well	versed	knowledge	ofPayments	system	and	Gateway	transaction	\n	Well	versed	inallphases	ofSoftware	Development	Life	Cycle	(SDLC)	and	Software	Testing	Life	Cycle	(STLC).	\n	Experience	inTesting	Web	Based	Application,	Windows	Based	Application	,Web-Service	and	Database	.	\n	Extensive	Knowledge	inwriting	Functional	and	Regression	Test-cases	,maintaining	and	modifying	test	cases.	\n	Proficient	inautomation	using	Selenium	Web-driver	with	Java	programming	language	.	\n	Extensive	knowledge	ofwriting	scripts	using	Element	locators	,Web-driver	methods	and	Java	Programming	.	\n	Generating	defect	report	and	interacting	with	developers	toresolve	the	defects.	\n	Having	good	exposure	todefect	management,	raising	defect/bugs.	\n	Having	good	time	management	towork	ondifferent	types	ofreleases	simultaneously	(BAU/OOC/Strategic).	\n	Has	rigorously	worked	onAPI	Testing	and	API	Test	Automation	with	JavaScript	-Mocha	Framework	\n	Extensive	knowledge	onvarious	testing	methodologies	like	UAT,	SIT,	Regression	testing,	Sanity	testing,	Smoke	testing,	and	\nFunctional	testing	.	\n	Experience	inusing	Test	Management	tool	HP-ALM,	JIRA	,Rally	.	\n	Great	exposure	toMS-OFFICE	suite	,Google	Suite	.	\n	Worked	extensively	onautomating	BFS	applications	from	enhancing	the	Automation	framework	inEclipse	using	Maven	tocommitting	\nthe	code	toBitBucket	/GITHUB	and	storing	test	artefacts	inJIRA	after	every	sprint	completion	.	\n	Have	working	experience	inCI/CD	model.	\n	Has	Good	Understanding	onEvent	Driven	System,	that	takes	data	asevent	process	it.	\n	Worked	onsetting	upKAFKA	and	KINESIS	for	Data	Stream	Processing	\n	Worked	onBuilding	Data	Pipeline	inPython	and	Deploying	itonAWS	Lambda	\n	Has	good	interpersonal,	communication	and	organizational	skill	with	ability	tointeract	effectively	with	colleagues	atalllevel.	\nTech	Stack:\nLanguages:	Python,	Core	Java,	SQL	,Javascript-NodeJS	\nOperating	System:	Windows,	Linux	,MacOS	\nTest	Management	Tool:	HP-ALM,	JIRA	,Rally	\nData	Base	Tools:	DBeaver,	MySQL,	Microsoft	SQL	Server	.,Google	Big	Query,	ElasticSearch	\nKnowledge	And	Worked	On:	API	Testing	with	POSTMAN,	API	Performance	Testing	with	JMeter	,ETL	Testing,	GCP,	AWS	EC2,	AWS	Lambda,	\nAWS	KINESIS,	Kafka,	Data	Streaming	Project,	API	Contract	Testing,	Docker	\nVersioning	Tool:	GIT	\nCertification:	GIT	(Udemy)	,AWS	API	GATEWAY(AWS),	GCP	KAFKA(GOOGLE),	Streaming	Pipeline	Using	Pyspark	and	Kakfa	\nAutomation	Framework	:BDD	-Cucumber,	TDD-	TestNG,	JavaScript	Mocha	\nAPI	Development	Framework	:Fast	API	Python	\nDeployment	Framework	:Zappa	\nAutomation	Tools:	Selenium	Web-driver,	Jenkins	,TestNG,	Cucumber	\nDeployment	Stack:	Kubernetes,	AWS	EKS	\nEmployment	Detail:	\nName	OfCompany	:Onclusive,	Mumbai	\nDesignation	:Senior	Software	Engineer	(Data	Engineer)	\nDuration	:19Oct	2022	ToCurrent	\nName	OfCompany	:TSYS	,Pune	\nDesignation	:Automation	Developer	\nDuration	:17May	2021	To	18Oct	2022	\nName	OfCompany	:Altimetrik,	Pune	\nDesignation	:Automation	Quality	Engineer	\nDuration	:18Jan	2021	To15May	2021	\nName	ofCompany	:Cognizant	Technology,	Pune	\nDesignation	:Test	Professional	\nDuration	:26June	2018	TO	15Jan	2021\n\nOnclusive	\nProject	1:	\nName	:Data	Platform	\nDesignation	:Senior	Software	Engineer	\nProject	Description	:Onclusive	isaMedia	Monitoring	Company.	Which,	monitors	the	Articles	,Content,	News,	Radio,	Youtube	and	also	TV	\nChannel.	Tofind	out	the	sentiment	for	the	particular	product	inmarket.	\nWhere	we	have	multiple	sources	who	pushes	Petabyte	ofdata	into	our	system	we	stream	that	into	MSK	Kafka	and	then	into	AWS	Kinesis	,using	\nData	Pipeline	asaconsumer	we	fetch	itand	process	ittoElastic	Search.(Elastic	stack)	\nAfterwards	this	used	for	sentiment	analysis	ofmarket	and	further,	using	API	this	Data	isfetch	and	given	toDown-Stream	Application	for	ML/AI	\npipeline.\nRole/Responsibility:	\n	ToDevelop	API	that	connects	with	Elastic-Search	and	fetch	out	data	with	relevant	query	that	we	build	run	time	.	\n	We	dohave	lots	ofAPI	that	exports	the	for	articles	based	onuser-query,	Iworked	onDeveloping	and	maintaining	API	\n	Todevelop	Data	Pipeline	Fetching	data	from	Apache	Kafka	and	AWS	Kinesis	then	transforming	and	Pushing	ittoStorage.(with	hosted	\nservice	onAWS	Lambda)	\n	Interm	tomodernize	these	pipeline	and	improving	the	performance	we	have	shifted	toApache	Flink	and	Apache	Beam	Pipeline	\no	Where	itfetch	data	from	Kafka,	using	UDF	we	modify	and	transform	this	data	and	push	itback	toElastic	Sink.	\no	Elastic	Sink	isself	managed	service	that	push	data	toElastic	Search	\n	Responsible	for	developing	and	deploying	the	API-P	oller	onAWS	ECS	tofetch	data	from	various	API	sources,	transform	it,and	store	it	\ninto	S3before	pushing	ittoKinesis	for	further	processing.	\nOther	Responsibilites	:	\n	Taking	part	inDesigning	Data	Pipeline	\n	Modernizing	Data	Pipelines	\no	Earlier	we	were	using	AWS	Kinesis	for	our	Data	Streaming,	now	we	have	designed	the	pipeline	touse	Kafka	which	has	good	\nRetention	period	and	also	tomaintain	the	AWS	Cost	itwill	becheaper	than	Kinesis.	\n	Adding	Layer	for	Open-Telemetry	intoServerless	AWS	Lambda	tocapture	telemetry	data	for	Failure	Analysis	\n	Deploying	StateFullset	ElasticSearch	InKubernetes	\n	Streaming	Data	Processing	On	Apache	Beam	Pipeline	\nProject	2:	\nName	:Data	Platform	\nDesignation	:Automation	Developer	\nProject	Description	:Onclusive	isaMedia	Monitoring	Company.	Which,	monitors	the	Articles	,Content,	News,	Radio,	Youtube	and	also	TV	\nChannel.	Tofind	out	the	sentiment	for	the	particular	product	inmarket.	\nWhere	we	have	multiple	sources	who	pushes	Petabyte	ofdata	into	our	system	we	stream	that	into	MSK	Kafka	and	then	into	AWS	Kinesis	,using	\nBroadcast	pipeline	asaconsumer	we	fetch	itand	process	ittoElasticSearch.	\nAfterwards	this	used	for	sentiment	analysis	ofmarket	and	further,	using	API	this	Data	isfetch	and	given	toDown-Stream	Application	for	ML/AI	\npipeline.\nRole/Responsibility:	Worked	asaAutomation	Architect	\n	ToDevelop	API	Automation	Framework	based	onNodeJS-JavaScript.	\n	Inclusion	ofBDD	inNode-JS	Framework.	\n	Maintaining	Automation	Framework	asaQA	Engineer	for	allthe	In-scope	API	\n	Completing	Testing	using	Manual	Testcases	and	Automation	\nOther	Responsibilites	:	\n	Taking	part	inDesigning	Data	Pipeline	\n	Modernizing	Data	Pipelines	\no	Earlier	we	were	using	AWS	Kinesis	for	our	Data	Streaming,	now	we	have	designed	the	pipeline	touse	Kafka	which	has	good	\nRetention	period	and	also	tomaintain	the	AWS	Cost	itwill	becheaper	than	Kinesis.	\n	Adding	Layer	for	Open-Telemetry	intoServerless	AWS	Lambda	tocapture	telemetry	data	for	Failure	Analysis\n\nTSYS	:Project	Worked	On	\nProject	1:	\nName	:UCP	-Global	Payments	\nDesignation	:Automation	Developer	\nProject	Description	:	\nAsaglobal	payments	we	wants	our	Merchants	has	full	capabilities	toselect	their	transaction	gateway	asper	their	own	\nconvenience	sothat	they	can	decide	their	security	level	and	transaction	changes	asper	area	they	are	serving	.Asaresult	we	have	created	aGlobal	\nPlatform	when	Global	Payment	will	board	Merchants	onUnified	Platform	(Unified	Commerce	Platform)	that	itwill	gives	right	toMerchants	toselect	\ngateway	without	any	code	changes.	\nHere	we	asaGlobal	Payments	will	take	care	ofinternal	transformation	and	Merchant	just	need	toplug	out	APIs	and	start	\nplaying	the	business	without	worrying	about	their	internal	transaction	transformation.	\nProject	2:	\nName	:Propay	Enterprise	Integration	\nDesignation	:Automation	Developer	\nProject	Description	:	\nAsTSYS,	AGlobal	Payments	Company,	we	act	asamediator	between	payment	gateways,	platform	and	Merchant.	\nMindBody	asaclient	comes	toGlobal	Payments	with	ask	that	they	want	tomigrate	their	allMerchant	toPROPAY	platform.	We	GPAPI	handles	their	\nTransaction,	Merchant	Boarding	Requirement	and	other	POS	devices	requirement	within	Single	Request.	\nArchitecture	works	like	take	abase	request	from	Merchant(Mindbody	Merchant)	segregate	and	convert	that	request	into	required	request	form	\nDownstream	I.ePROPAY,	TransIT,	SoundPayment,	PAX	Portal,	Express.	Receive	the	response	and	send	ittomerchant.	\nAsnumber	ofFinancial	entities	comes	into	picture	with	this	project,	ithas	avery	huge	scope.	Itincludes	payment	processing	and	merchant	\nprocessing.\nRole/Responsibility:	\n	Asbeing	aintegral	part	ofEnterprise	Integration	Team	my	responsibilities	were	related	togather	and	understand	the	latest	\nchanges/requirements	for	the	Gateway	Release	.	\n	Write	functional	and	regression	test	cases.	Used	POSTMAN	for	API	Testing	\n	Execute	manually	functional	and	regression	pack,	manage	test	cases	inRally	\n	Raise	defect	and	communicate	with	developers	toresolve	it.	\n	Reporting	Bugs,	verifying	logs,	verifying	server-side	services,	verifying	data,	generating	SQL	scripts	for	Database	changes.	\n	Verify	other	dependent	systems	are	receiving	proper	transaction	message	and	help	them	for	payments	booking	scenarios.	\n	Create	and	Execute	Automat	ion	Test	Scripts,	inNodeJS	based	framework	inJavaScript	.	\n	Verify	API-Integration	working	asexpected	\n	API	Performance	testing	with	JMete	r	\n	Generating	Sign-off	report	with	notifying	that	everything	isworking	fine	with	allnew	changes,	sothat	allnew	requirements	should	be	\nlive	inproduction	inupcoming	bi-weekly	release.	\n	Handling	multiple	developers	overseas.	\n	Communicating	with	Dev-ops	team	tocheck	new	build-changes,	DB-changes,	services	side	changes.	\n	Experience	inworking	with	Agile	and	CI/CD	testing	Models	.	\nAltimetrik	:Projet	Worked	On	:	\nProject	:	\nName	:CitiBank	-High	Value	Payments	\nDesignation	:Quality	Engineer	\nProject	Description	:	\nFew	High	Valued	Customers	are	tied	up	with	CitiBank,	they	transact	huge	amount	ofmoney	per	Business	Day.	We	have	\ncreated	DashBoards	tokeep	atrack	ontheir	transactions	and	check	and	observe	what	isthe	status	for	that	payment	and	transactions,	Inthe	form	\nonGraphs	and	Tabular	Data	we	are	displaying	data	.	\nRoles/Responsibility	:	\n	Write	Test	Cases	For	various	Dash-Board	showing	different	Types	ofData	Analysis	for	Payments.	\n	Analyse	Requirement	From	JIRA/BRD/FRD	\n	DB	Testing	with	high	number	ofHigh	Valued	Payment	with	Different	Types	OfMOP	(Mode	OfPayments)	\n	UITesting	-Verify	Data	Calculation	and	computation	for	the	Data	from	DB	iscorrect.	\n	Functional	And	Regression	Testing.	\n	Create	UIAutomation	Framework	Based	onJava	Selenium	BDD	-Cucumber.\n\nCognizant	:Project	Worked	On:	\nProject	1:	\nName	:Global	Markets	(Global	Securities	Lending)-	Trade	Execution	and	Management	(Short	&ndash;Selling)	\nClient:	Credit	Suisse	\nProject	Type	:Functional	And	Automation	Testing	\nDuration:	24Sept	2018	toJan	2021	\nProject	Description:	\nCredit	Suisse	uses	anapplication	toBook	trades	for	Short	Selling.	Where	Borrow,	Loan	and	Collateral	Trades	were	booked	\nevery	day,	this	isavery	heavy	application	asitisused	bynumber	ofusers	allover	the	Globe.	\nRole/Responsibility:	\n	Togather	and	understand	the	latest	changes/requirements	for	the	release	from	Business	Requirement	Documents.	\n	Write	functional	and	regression	test	cases.	\n	Execute	manually	functional	and	regression	pack,	manage	test	cases	inQuality	Centre	-HP	ALM	\n	Raise	defect	and	communicate	with	developers	toresolve	it.	\n	Reporting	Bugs,	verifying	logs,	verifying	server-side	services,	verifying	data,	generating	SQL	scripts	for	Database	changes.	\n	Verify	other	dependent	systems	are	receiving	proper	trade	message	and	help	them	for	trade	booking	scenarios.	\n	Create	and	Execute	Automated	Test	Scripts,	and	verifying	results.	\n	Verify	API-Integration	working	asexpected	\n	API	Performance	testing	with	JMete	r	\n	Generating	Sign-off	report	with	notifying	that	everything	isworking	fine	with	allnew	changes,	sothat	allnew	requirements	should	be	\nlive	inproduction	inupcoming	bi-weekly	release.	\n	Taken	initiative	toenhance	automation	ofWeb	Based	Application	using	Selenium	WebDriver	with	IDE	Eclipse	.		Handling	multiple	developers	overseas.	\n	Communicating	with	Dev-ops	team	tocheck	new	build-changes,	DB-changes,	services	side	changes.	\n	Experience	inworking	with	Agile	and	CI/CD	testing	Models	.	\nProject	2:	\nName	:Security	Financing	Transaction	Regulation	\nClient:	Credit	Suisse	\nProject	Type	:Functional	Testing	\nDuration:	Jan	2020	toJan	2021	\nProject	Description:	\nThis	isaglobal	change	where	every	Investment	Bank,	needs	toreport	their	allEurope	based	transaction,	tothe	certain	\nReporting	Entity.	\nRole/Responsibility:	\n	Togather	and	understand	the	change	from	Business	Requirement	Documents.	\n	Write	functional	and	regression	test	cases.	\n	Execute	manually	functional	and	regression	pack.	\n	Raise	defect	and	communicate	with	developers	toresolve	it.	\n	Verifying	logs,	verifying	server-side	services,	verifying	data,	generating	SQL	scripts	for	Database	testing.	\n	Verify	other	dependent	systems	are	receiving	proper	trade	message	and	help	them	for	trade	booking	scenarios.	\nProject	3:	\nName:	Fixed	Income	Securities	\nClient:	Credit	Suisse	\nProject	Type:	Functional	Testing/API	Testing	\nDuration:	March	2020	toMay	2020	\nProject	Description:	\nFixed	Income	securities	tobeimplemented	for	certain	Client/Counterparties	with	specified	entity	directions,	\nFor	that	combination	ofClient	and	Fixed	Income	Securities	system	calculating	rate	and	fetching	them	from	API	end	point.\n\nRole/Responsibility:	\n	Togather	and	understand	the	changes.	\n	Verify	API-Integration	working	asexpected.	\n	Write	and	execute	manual	and	regression	pack.	\n	Verify	logs,	DB	testing	reporting	defect	\n	Performance	testing	for	API	with	JMeter	.	\nAcademic	Details:	\nSr.No	Qualification	University/Board	Passing	Year	Percentage	\n1.	Executive	PDGM	MIT	SDE	2022	Grade	A	\n2.	Bachelor	inComputer	Application	Amravati	University	2017	68%	\n3.	HSC	Maharashtra	State	Board	2014	62.46%	\n4.	SSC	Maharashtra	State	Board	2012	82.55%	\nOther	Qualification	Details:	\nSerialNo.	Qualification	University	/Board	Passing	Year	Percentage	\n1.	Eng-Typing	30	Maharashtra	State	Council	ofExamination,	Pune	Dec-2012	65%	\n2.	Eng-Typing	40	Maharashtra	State	Council	ofExamination,	Pune	Dec-2012	51%	\n3.	MS-CIT	Maharashtra	State	Board	OfTechnical	Education,Mumbai	July-2010	92%	\nSelf-Learning	Project:\n	Project	1:Text	files	reader.	(Text	tospeech).	(Using	python	and	GTTS	library)		Project	2:QR	code	generator	(Using	Python)		Project	3:Speech	totext	(Using	Python	and	Google	Speech	Recognition	Library)	\nAchievement:	\n	Awarded	as&ldquo;Pillar	OfThe	Month	&ldquo;(Cognizant	May	2019)		Best	&ldquo;Student	ofYear	&rdquo;(CMCS	2017)	\nPersonal	Details:	\nName:	Bhushan	Ashokrao	Bende	Dob:	18/01/1996	Permanent	Address:	Old	Gilani	Nagar,	Yavatmal	445001	Maharashtra	Current	Address:	Venkat-Padmavati	PGbehind	Persistent,	Hinjewadi	,411057	Pune	Strength:	Good	Communication,	Quick	learner	Hobby:	Cooking,	Gardening	Languages	known:	English,	Marathi,	Hindi	\nMobile	+91	9405708908	\nEmail:	Primary:	bhushanyahoo@gmail.com	\nDeclaration:	Ihereby	declare	that	the	above	information	istrue	tothe	best	ofmy	knowledge	and	allnecessary	documents	will	beprovided	as&amp;	\nwhen	required.	\nSignature:	Bhushan	Ashokrao	Bende	\nDate:	Place:	Pune',2,2,1,'Recruiter',NULL,'N',14,0,'2024-07-22 04:53:18','2024-07-19 04:53:18',0,NULL,NULL,'2024-07-19 04:50:38',0),(411,98,1,4,NULL,'1_98_3441068586699f950dcc08_Bhushan_Bende_Developer_Connected_Platform_(1).pdf','Bhushan Bende_Developer_Connected Platform (1).pdf','Bhushan	Bende	\nSoftware	Engineer	\nRoles	:Senior	Software	Engineer	\nE-mail:	bhushanyahoo@gmail.com	\nMobile:	9405708908	\nProfessional	Synopsis:	\n	Experience	inPython	Developer	,Building	APIand	Data	Pipeline	,with	PRDomain	\n	Experience	onworking/building	Python-Fast	API	hosted	onLambda	connected	with	Elastic	Search	\n	Building	ETL	&amp;ELT	Data	Pipeline	inPython	with	AWS	Managed	Streaming	Application	Flink,	Self	Managed	Flink	and	AWS	Lambda	\n	5years	ofFunctional	and	Automation	Testing	experience	inInvestment	Banking	Domain	,Payment	Domain	and	Media	Monitoring.	\n	Keen	understanding	onCapital	Market	and	Money	Market.	\n	Well	versed	knowledge	ofPayments	system	and	Gateway	transaction	\n	Well	versed	inallphases	ofSoftware	Development	Life	Cycle	(SDLC)	and	Software	Testing	Life	Cycle	(STLC).	\n	Experience	inTesting	Web	Based	Application,	Windows	Based	Application	,Web-Service	and	Database	.	\n	Extensive	Knowledge	inwriting	Functional	and	Regression	Test-cases	,maintaining	and	modifying	test	cases.	\n	Proficient	inautomation	using	Selenium	Web-driver	with	Java	programming	language	.	\n	Extensive	knowledge	ofwriting	scripts	using	Element	locators	,Web-driver	methods	and	Java	Programming	.	\n	Generating	defect	report	and	interacting	with	developers	toresolve	the	defects.	\n	Having	good	exposure	todefect	management,	raising	defect/bugs.	\n	Having	good	time	management	towork	ondifferent	types	ofreleases	simultaneously	(BAU/OOC/Strategic).	\n	Has	rigorously	worked	onAPI	Testing	and	API	Test	Automation	with	JavaScript	-Mocha	Framework	\n	Extensive	knowledge	onvarious	testing	methodologies	like	UAT,	SIT,	Regression	testing,	Sanity	testing,	Smoke	testing,	and	\nFunctional	testing	.	\n	Experience	inusing	Test	Management	tool	HP-ALM,	JIRA	,Rally	.	\n	Great	exposure	toMS-OFFICE	suite	,Google	Suite	.	\n	Worked	extensively	onautomating	BFS	applications	from	enhancing	the	Automation	framework	inEclipse	using	Maven	tocommitting	\nthe	code	toBitBucket	/GITHUB	and	storing	test	artefacts	inJIRA	after	every	sprint	completion	.	\n	Have	working	experience	inCI/CD	model.	\n	Has	Good	Understanding	onEvent	Driven	System,	that	takes	data	asevent	process	it.	\n	Worked	onsetting	upKAFKA	and	KINESIS	for	Data	Stream	Processing	\n	Worked	onBuilding	Data	Pipeline	inPython	and	Deploying	itonAWS	Lambda	\n	Has	good	interpersonal,	communication	and	organizational	skill	with	ability	tointeract	effectively	with	colleagues	atalllevel.	\nTech	Stack:\nLanguages:	Python,	Core	Java,	SQL	,Javascript-NodeJS	\nOperating	System:	Windows,	Linux	,MacOS	\nTest	Management	Tool:	HP-ALM,	JIRA	,Rally	\nData	Base	Tools:	DBeaver,	MySQL,	Microsoft	SQL	Server	.,Google	Big	Query,	ElasticSearch	\nKnowledge	And	Worked	On:	API	Testing	with	POSTMAN,	API	Performance	Testing	with	JMeter	,ETL	Testing,	GCP,	AWS	EC2,	AWS	Lambda,	\nAWS	KINESIS,	Kafka,	Data	Streaming	Project,	API	Contract	Testing,	Docker	\nVersioning	Tool:	GIT	\nCertification:	GIT	(Udemy)	,AWS	API	GATEWAY(AWS),	GCP	KAFKA(GOOGLE),	Streaming	Pipeline	Using	Pyspark	and	Kakfa	\nAutomation	Framework	:BDD	-Cucumber,	TDD-	TestNG,	JavaScript	Mocha	\nAPI	Development	Framework	:Fast	API	Python	\nDeployment	Framework	:Zappa	\nAutomation	Tools:	Selenium	Web-driver,	Jenkins	,TestNG,	Cucumber	\nDeployment	Stack:	Kubernetes,	AWS	EKS	\nEmployment	Detail:	\nName	OfCompany	:Onclusive,	Mumbai	\nDesignation	:Senior	Software	Engineer	(Data	Engineer)	\nDuration	:19Oct	2022	ToCurrent	\nName	OfCompany	:TSYS	,Pune	\nDesignation	:Automation	Developer	\nDuration	:17May	2021	To	18Oct	2022	\nName	OfCompany	:Altimetrik,	Pune	\nDesignation	:Automation	Quality	Engineer	\nDuration	:18Jan	2021	To15May	2021	\nName	ofCompany	:Cognizant	Technology,	Pune	\nDesignation	:Test	Professional	\nDuration	:26June	2018	TO	15Jan	2021\n\nOnclusive	\nProject	1:	\nName	:Data	Platform	\nDesignation	:Senior	Software	Engineer	\nProject	Description	:Onclusive	isaMedia	Monitoring	Company.	Which,	monitors	the	Articles	,Content,	News,	Radio,	Youtube	and	also	TV	\nChannel.	Tofind	out	the	sentiment	for	the	particular	product	inmarket.	\nWhere	we	have	multiple	sources	who	pushes	Petabyte	ofdata	into	our	system	we	stream	that	into	MSK	Kafka	and	then	into	AWS	Kinesis	,using	\nData	Pipeline	asaconsumer	we	fetch	itand	process	ittoElastic	Search.(Elastic	stack)	\nAfterwards	this	used	for	sentiment	analysis	ofmarket	and	further,	using	API	this	Data	isfetch	and	given	toDown-Stream	Application	for	ML/AI	\npipeline.\nRole/Responsibility:	\n	ToDevelop	API	that	connects	with	Elastic-Search	and	fetch	out	data	with	relevant	query	that	we	build	run	time	.	\n	We	dohave	lots	ofAPI	that	exports	the	for	articles	based	onuser-query,	Iworked	onDeveloping	and	maintaining	API	\n	Todevelop	Data	Pipeline	Fetching	data	from	Apache	Kafka	and	AWS	Kinesis	then	transforming	and	Pushing	ittoStorage.(with	hosted	\nservice	onAWS	Lambda)	\n	Interm	tomodernize	these	pipeline	and	improving	the	performance	we	have	shifted	toApache	Flink	and	Apache	Beam	Pipeline	\no	Where	itfetch	data	from	Kafka,	using	UDF	we	modify	and	transform	this	data	and	push	itback	toElastic	Sink.	\no	Elastic	Sink	isself	managed	service	that	push	data	toElastic	Search	\n	Responsible	for	developing	and	deploying	the	API-P	oller	onAWS	ECS	tofetch	data	from	various	API	sources,	transform	it,and	store	it	\ninto	S3before	pushing	ittoKinesis	for	further	processing.	\nOther	Responsibilites	:	\n	Taking	part	inDesigning	Data	Pipeline	\n	Modernizing	Data	Pipelines	\no	Earlier	we	were	using	AWS	Kinesis	for	our	Data	Streaming,	now	we	have	designed	the	pipeline	touse	Kafka	which	has	good	\nRetention	period	and	also	tomaintain	the	AWS	Cost	itwill	becheaper	than	Kinesis.	\n	Adding	Layer	for	Open-Telemetry	intoServerless	AWS	Lambda	tocapture	telemetry	data	for	Failure	Analysis	\n	Deploying	StateFullset	ElasticSearch	InKubernetes	\n	Streaming	Data	Processing	On	Apache	Beam	Pipeline	\nProject	2:	\nName	:Data	Platform	\nDesignation	:Automation	Developer	\nProject	Description	:Onclusive	isaMedia	Monitoring	Company.	Which,	monitors	the	Articles	,Content,	News,	Radio,	Youtube	and	also	TV	\nChannel.	Tofind	out	the	sentiment	for	the	particular	product	inmarket.	\nWhere	we	have	multiple	sources	who	pushes	Petabyte	ofdata	into	our	system	we	stream	that	into	MSK	Kafka	and	then	into	AWS	Kinesis	,using	\nBroadcast	pipeline	asaconsumer	we	fetch	itand	process	ittoElasticSearch.	\nAfterwards	this	used	for	sentiment	analysis	ofmarket	and	further,	using	API	this	Data	isfetch	and	given	toDown-Stream	Application	for	ML/AI	\npipeline.\nRole/Responsibility:	Worked	asaAutomation	Architect	\n	ToDevelop	API	Automation	Framework	based	onNodeJS-JavaScript.	\n	Inclusion	ofBDD	inNode-JS	Framework.	\n	Maintaining	Automation	Framework	asaQA	Engineer	for	allthe	In-scope	API	\n	Completing	Testing	using	Manual	Testcases	and	Automation	\nOther	Responsibilites	:	\n	Taking	part	inDesigning	Data	Pipeline	\n	Modernizing	Data	Pipelines	\no	Earlier	we	were	using	AWS	Kinesis	for	our	Data	Streaming,	now	we	have	designed	the	pipeline	touse	Kafka	which	has	good	\nRetention	period	and	also	tomaintain	the	AWS	Cost	itwill	becheaper	than	Kinesis.	\n	Adding	Layer	for	Open-Telemetry	intoServerless	AWS	Lambda	tocapture	telemetry	data	for	Failure	Analysis\n\nTSYS	:Project	Worked	On	\nProject	1:	\nName	:UCP	-Global	Payments	\nDesignation	:Automation	Developer	\nProject	Description	:	\nAsaglobal	payments	we	wants	our	Merchants	has	full	capabilities	toselect	their	transaction	gateway	asper	their	own	\nconvenience	sothat	they	can	decide	their	security	level	and	transaction	changes	asper	area	they	are	serving	.Asaresult	we	have	created	aGlobal	\nPlatform	when	Global	Payment	will	board	Merchants	onUnified	Platform	(Unified	Commerce	Platform)	that	itwill	gives	right	toMerchants	toselect	\ngateway	without	any	code	changes.	\nHere	we	asaGlobal	Payments	will	take	care	ofinternal	transformation	and	Merchant	just	need	toplug	out	APIs	and	start	\nplaying	the	business	without	worrying	about	their	internal	transaction	transformation.	\nProject	2:	\nName	:Propay	Enterprise	Integration	\nDesignation	:Automation	Developer	\nProject	Description	:	\nAsTSYS,	AGlobal	Payments	Company,	we	act	asamediator	between	payment	gateways,	platform	and	Merchant.	\nMindBody	asaclient	comes	toGlobal	Payments	with	ask	that	they	want	tomigrate	their	allMerchant	toPROPAY	platform.	We	GPAPI	handles	their	\nTransaction,	Merchant	Boarding	Requirement	and	other	POS	devices	requirement	within	Single	Request.	\nArchitecture	works	like	take	abase	request	from	Merchant(Mindbody	Merchant)	segregate	and	convert	that	request	into	required	request	form	\nDownstream	I.ePROPAY,	TransIT,	SoundPayment,	PAX	Portal,	Express.	Receive	the	response	and	send	ittomerchant.	\nAsnumber	ofFinancial	entities	comes	into	picture	with	this	project,	ithas	avery	huge	scope.	Itincludes	payment	processing	and	merchant	\nprocessing.\nRole/Responsibility:	\n	Asbeing	aintegral	part	ofEnterprise	Integration	Team	my	responsibilities	were	related	togather	and	understand	the	latest	\nchanges/requirements	for	the	Gateway	Release	.	\n	Write	functional	and	regression	test	cases.	Used	POSTMAN	for	API	Testing	\n	Execute	manually	functional	and	regression	pack,	manage	test	cases	inRally	\n	Raise	defect	and	communicate	with	developers	toresolve	it.	\n	Reporting	Bugs,	verifying	logs,	verifying	server-side	services,	verifying	data,	generating	SQL	scripts	for	Database	changes.	\n	Verify	other	dependent	systems	are	receiving	proper	transaction	message	and	help	them	for	payments	booking	scenarios.	\n	Create	and	Execute	Automat	ion	Test	Scripts,	inNodeJS	based	framework	inJavaScript	.	\n	Verify	API-Integration	working	asexpected	\n	API	Performance	testing	with	JMete	r	\n	Generating	Sign-off	report	with	notifying	that	everything	isworking	fine	with	allnew	changes,	sothat	allnew	requirements	should	be	\nlive	inproduction	inupcoming	bi-weekly	release.	\n	Handling	multiple	developers	overseas.	\n	Communicating	with	Dev-ops	team	tocheck	new	build-changes,	DB-changes,	services	side	changes.	\n	Experience	inworking	with	Agile	and	CI/CD	testing	Models	.	\nAltimetrik	:Projet	Worked	On	:	\nProject	:	\nName	:CitiBank	-High	Value	Payments	\nDesignation	:Quality	Engineer	\nProject	Description	:	\nFew	High	Valued	Customers	are	tied	up	with	CitiBank,	they	transact	huge	amount	ofmoney	per	Business	Day.	We	have	\ncreated	DashBoards	tokeep	atrack	ontheir	transactions	and	check	and	observe	what	isthe	status	for	that	payment	and	transactions,	Inthe	form	\nonGraphs	and	Tabular	Data	we	are	displaying	data	.	\nRoles/Responsibility	:	\n	Write	Test	Cases	For	various	Dash-Board	showing	different	Types	ofData	Analysis	for	Payments.	\n	Analyse	Requirement	From	JIRA/BRD/FRD	\n	DB	Testing	with	high	number	ofHigh	Valued	Payment	with	Different	Types	OfMOP	(Mode	OfPayments)	\n	UITesting	-Verify	Data	Calculation	and	computation	for	the	Data	from	DB	iscorrect.	\n	Functional	And	Regression	Testing.	\n	Create	UIAutomation	Framework	Based	onJava	Selenium	BDD	-Cucumber.\n\nCognizant	:Project	Worked	On:	\nProject	1:	\nName	:Global	Markets	(Global	Securities	Lending)-	Trade	Execution	and	Management	(Short	&ndash;Selling)	\nClient:	Credit	Suisse	\nProject	Type	:Functional	And	Automation	Testing	\nDuration:	24Sept	2018	toJan	2021	\nProject	Description:	\nCredit	Suisse	uses	anapplication	toBook	trades	for	Short	Selling.	Where	Borrow,	Loan	and	Collateral	Trades	were	booked	\nevery	day,	this	isavery	heavy	application	asitisused	bynumber	ofusers	allover	the	Globe.	\nRole/Responsibility:	\n	Togather	and	understand	the	latest	changes/requirements	for	the	release	from	Business	Requirement	Documents.	\n	Write	functional	and	regression	test	cases.	\n	Execute	manually	functional	and	regression	pack,	manage	test	cases	inQuality	Centre	-HP	ALM	\n	Raise	defect	and	communicate	with	developers	toresolve	it.	\n	Reporting	Bugs,	verifying	logs,	verifying	server-side	services,	verifying	data,	generating	SQL	scripts	for	Database	changes.	\n	Verify	other	dependent	systems	are	receiving	proper	trade	message	and	help	them	for	trade	booking	scenarios.	\n	Create	and	Execute	Automated	Test	Scripts,	and	verifying	results.	\n	Verify	API-Integration	working	asexpected	\n	API	Performance	testing	with	JMete	r	\n	Generating	Sign-off	report	with	notifying	that	everything	isworking	fine	with	allnew	changes,	sothat	allnew	requirements	should	be	\nlive	inproduction	inupcoming	bi-weekly	release.	\n	Taken	initiative	toenhance	automation	ofWeb	Based	Application	using	Selenium	WebDriver	with	IDE	Eclipse	.		Handling	multiple	developers	overseas.	\n	Communicating	with	Dev-ops	team	tocheck	new	build-changes,	DB-changes,	services	side	changes.	\n	Experience	inworking	with	Agile	and	CI/CD	testing	Models	.	\nProject	2:	\nName	:Security	Financing	Transaction	Regulation	\nClient:	Credit	Suisse	\nProject	Type	:Functional	Testing	\nDuration:	Jan	2020	toJan	2021	\nProject	Description:	\nThis	isaglobal	change	where	every	Investment	Bank,	needs	toreport	their	allEurope	based	transaction,	tothe	certain	\nReporting	Entity.	\nRole/Responsibility:	\n	Togather	and	understand	the	change	from	Business	Requirement	Documents.	\n	Write	functional	and	regression	test	cases.	\n	Execute	manually	functional	and	regression	pack.	\n	Raise	defect	and	communicate	with	developers	toresolve	it.	\n	Verifying	logs,	verifying	server-side	services,	verifying	data,	generating	SQL	scripts	for	Database	testing.	\n	Verify	other	dependent	systems	are	receiving	proper	trade	message	and	help	them	for	trade	booking	scenarios.	\nProject	3:	\nName:	Fixed	Income	Securities	\nClient:	Credit	Suisse	\nProject	Type:	Functional	Testing/API	Testing	\nDuration:	March	2020	toMay	2020	\nProject	Description:	\nFixed	Income	securities	tobeimplemented	for	certain	Client/Counterparties	with	specified	entity	directions,	\nFor	that	combination	ofClient	and	Fixed	Income	Securities	system	calculating	rate	and	fetching	them	from	API	end	point.\n\nRole/Responsibility:	\n	Togather	and	understand	the	changes.	\n	Verify	API-Integration	working	asexpected.	\n	Write	and	execute	manual	and	regression	pack.	\n	Verify	logs,	DB	testing	reporting	defect	\n	Performance	testing	for	API	with	JMeter	.	\nAcademic	Details:	\nSr.No	Qualification	University/Board	Passing	Year	Percentage	\n1.	Executive	PDGM	MIT	SDE	2022	Grade	A	\n2.	Bachelor	inComputer	Application	Amravati	University	2017	68%	\n3.	HSC	Maharashtra	State	Board	2014	62.46%	\n4.	SSC	Maharashtra	State	Board	2012	82.55%	\nOther	Qualification	Details:	\nSerialNo.	Qualification	University	/Board	Passing	Year	Percentage	\n1.	Eng-Typing	30	Maharashtra	State	Council	ofExamination,	Pune	Dec-2012	65%	\n2.	Eng-Typing	40	Maharashtra	State	Council	ofExamination,	Pune	Dec-2012	51%	\n3.	MS-CIT	Maharashtra	State	Board	OfTechnical	Education,Mumbai	July-2010	92%	\nSelf-Learning	Project:\n	Project	1:Text	files	reader.	(Text	tospeech).	(Using	python	and	GTTS	library)		Project	2:QR	code	generator	(Using	Python)		Project	3:Speech	totext	(Using	Python	and	Google	Speech	Recognition	Library)	\nAchievement:	\n	Awarded	as&ldquo;Pillar	OfThe	Month	&ldquo;(Cognizant	May	2019)		Best	&ldquo;Student	ofYear	&rdquo;(CMCS	2017)	\nPersonal	Details:	\nName:	Bhushan	Ashokrao	Bende	Dob:	18/01/1996	Permanent	Address:	Old	Gilani	Nagar,	Yavatmal	445001	Maharashtra	Current	Address:	Venkat-Padmavati	PGbehind	Persistent,	Hinjewadi	,411057	Pune	Strength:	Good	Communication,	Quick	learner	Hobby:	Cooking,	Gardening	Languages	known:	English,	Marathi,	Hindi	\nMobile	+91	9405708908	\nEmail:	Primary:	bhushanyahoo@gmail.com	\nDeclaration:	Ihereby	declare	that	the	above	information	istrue	tothe	best	ofmy	knowledge	and	allnecessary	documents	will	beprovided	as&amp;	\nwhen	required.	\nSignature:	Bhushan	Ashokrao	Bende	\nDate:	Place:	Pune',2,2,1,'Recruiter','SL','N',14,1,'2024-07-22 05:38:03','2024-07-19 05:38:03',0,NULL,NULL,'2024-07-19 05:27:44',0),(412,99,73,45,NULL,'73_99_1603179284669a1cfada254_HMI_Developer-John_Stanish_Pandian.pdf','HMI Developer-John Stanish Pandian.pdf','Android Native Mobile App	lication	 	Developer	 	\n \nJOHN STANISH	 	\nChennai, Tamil Nadu	 	\n+91 8220004353	 	\ns	tan	y	john	9	3	@gmail.com	 	\n \nPROFILE &bull; ABOUT ME	 	\n \nA pioneering and security	-	oriented developer offering more than 	Eight	 	years&rsquo; experience designing, 	\nimplementing, integrating, testing and supporting impactful applications developed in diverse, collaborative \nenvironments for mobile devices on the Android platform	 	\n&bull;	 	Known for writing efficient, maintainable and reusable code that preserves privacy and security	 	\n&bull;	 	Expert in the interaction between various devices, screen sizes and different versions of Android	 	\n&bull;	 	Regarded as a self	-	motivated and well	-	organised team player	 	\n&bull;	 	Provided oversight and mentorship to a small team of developers	 	\n \nACADEMIC QUALIFICATION	 	\n 	\n&bull;	 	Completed B.E in the stream of Electronics and Communication in SRM Easwari Engineering \nCollege, Chennai with 8.02 CGPA, Year of completion 	-	 	2015.	 	\n&bull;	 	H.S.L.C in 	Start Matriculation Higher Secondary School, Tuticorin. Secured 93.75%. Year of 	\ncompletion 	-	 	2011.	 	\n&bull;	 	S.S.L.C in St. Joseph&rsquo;s Matriculation School, Tuticorin. Secured 89.5%. Year of completion 	-	 	2009.	 	\n \nWORK EXPERIENCE	 	\n 	\nRole &amp; Designation	 	Senior	 Analyst	 	&ndash;	 	Programmer	 	\nCompany	 	Ford Motor Company	 	\nDuration	 	Feb	 	2022	 	-	 	Present	 	\nLocation	 	CHENNAI, Tamil Nadu	 	\n \nTASKS AND RESPONSIBILITIES	 	\n&bull;	 	Develop native Android application using Kotlin in combi	nation with Jetpack Compose ensuring 	\nhigh	-	quality user experiences and seamless functionality	.	 	\n&bull;	 	Utilized Jira to effectively m	anage project tasks, track progress and facilitate efficient communication 	\namong team mem	bers	.	 	\n&bull;	 	Used Modern Android Development Libr	aries such as 	Jetpack Compose, Room	, Coroutines/Flow, 	\nDataStore	 	a	nd Da	gger	-	Hilt	 	\n&bull;	 	Authored J	u	nit 	tests using Mockito frameworks ensuring robust code quali	ty and minimizing 	\nregression issues.	 	\n&bull;	 	Applied design 	patterns such as MVVM to create scalable and mainta	ina	ble codebases 	improving 	\ncode organization and 	reusability.	 	\n \n 	\nC	oding	 	L	anguages	 	Java, Kotlin	 	\nD	atabase	 	SQLite, Room	 	\nVe r s i o n   C o n t r o l	 	GitHub	 	\nDevelopment T	ool	 	Android studio\n\nPROJECT EXPERIENCE	 	\n \nFordPass	 	&amp; 	Lincoln	 	Way	 	\n&bull;	 	Maintained, refined, and developed features for Ford&#039;s Ford	P	ass and 	Lincoln Way	 	automotive 	\napplications as part of a large team.	 	\n&bull;	 	Features I was responsible for include user vehicle authorization, 	A	dd vehicle, 	F	uel telemetry	, 	\nTPMS, 	Vehicle 	health, Vehicle Alerts, Vehicle Service related features, 	R	oad Side Assistance, 	\nCollision 	Assista	nce, 	Accounts which include	s 	Message 	centre	, Change 	Username	 	&amp; Password, 	\nVehicle feat	ure notifications, user 	information	, Delete Account	. 	 	\n&bull;	 	Rebuilt key features from a monolithic application to a modularized version that improved build \ntime.	 	\n&bull;	 	Utilized third party analytics to 	analyse	 	customer experience and track application stability.	 	\n&bull;	 	Work in an agile environment with a 	6	 	person android development feature team to consistently meet 	\ndeadlines for product deliverables while also providing support for existing features.	 	\n&bull;	 	Educated new hires about company best practices and paradigms to quickly develop the	 	necessary 	\nskills required to be a productive member of the team.	 	\n 	\nR	ole &amp; Designation	 	IT Analyst 	&ndash;	 Te a m  	Lead	 	\nCompany	 	TATA  C O N S U LTA N C Y  S E RV I C E S   LT D	 	\nDuration	 	Nov 2015 	&ndash;	 	Jan 2022	 	\nLocation	 	CHENNAI, Tamil Nadu	 	\n \nTASKS AND RESPONSIBILITIES	 	\n&bull;	 	C	onceptualised, designed, developed and deployed mobile applications for banking customers in 	\nGoogle play store and Apple store.	 	\n&bull;	 	Developed update patches, modules and components to mitigate security risks.	 	\n&bull;	 	L	ead	 	a team of 6, responsible for the development and maintenance of banking	 	applications in 	\nAndroid	.	 	\n \nSKILLS	 	\n&bull;	 	Expert at Android SDK design 	functionality, including the full activity lifecycle	.	 	\n&bull;	 	Clear and concise writer of communications and development documentation	.	 	\n&bull;	 	Strong Experience in OOPs Concepts, Exception handling	.	 	\n&bull;	 	Worked on Firebase Push Notifications, Crashlytics	 	and App distributions	.	 	\n&bull;	 	Well versed with Android Components like Services, Broadcast receivers, Fragments	 	and	 	a	ctivit	ies	.	 	\n&bull;	 	Worked on Location API, Geocoding	.	 	\n&bull;	 	Strong in web services using Retrofit library, performing fetch and sending data to server	.	 	\n&bull;	 	Knowledgeable about 	back	-	end development and documentation	.	 	\n&bull;	 	Worked on MVVM architecture, Offline storage (such as SQLite, Room)	.	 	\n&bull;	 	Having good knowledge of Android Jetpack Libraries (such as View Modal, Lifecycle, Live data	, 	\nData binding, Navigation, Fragments, Cards, Recycler view, Biomet	rics	)	, Kotlin Coroutines, RxJava, 	\nDependency injection using Dagger 2	.	 	\n 	\nC	oding	 	L	anguages	 	Java, Kotlin	 	\nD	atabase	 	SQLite, Room	 	\nVe r s i o n   C o n t r o l	 	GitHub	 	\nDevelopment T	ool	 	Android studio\n\nPROJECT EXPERIENCE	 	\n \nCUB mBank Plus (City Union Bank)	 	\n&bull;	 	Implemented push notifications using Firebase cloud messaging	 	 	\n&bull;	 	Redesigned the app with separate layouts and drawables to support multiple screen sizes and \ndensities	 	 	\n&bull;	 	Reduced application crashes by implementing Firebase Crashlytics, which helped in an increase in \nGoogle Play Store rating	 	\n&bull;	 	Enhanced the UI by using Google material design components	 	\n&bull;	 	Implemented OTP auto capture using 	SMS Retriever API	 	\n&bull;	 	Implemented Fingerprint based login authentication 	 	\n \nCUB BHIM UPI &amp; eWallet  (City Union Bank)	 	\n&bull;	 	Implemented QR Code Scanner	 	Using Camera and from Gallery using Google Zxing Library	 	\n&bull;	 	Experience in user specific QR Code generation 	for UPI payment	 	\n&bull;	 	Implemented User mobile number specific Device Binding.	 	\n&bull;	 	Integrated NPCI&rsquo;s UPI library for displaying 	payment 	authentication 	screen	 	\n&bull;	 	Experience in fetching the user location when they are using the application.	 	\n \nCUB Corporate Mobile (City Union Bank)	 	\n&bull;	 	Experience in password hashing using SHA1 &amp; SHA512	 	\n&bull;	 	Experience in request and response Encryption and decryption using AES algorithms 	 	\n&bull;	 	Implemented SSL Pinning for 	establishing a secure connection.	 	\n&bull;	 	Developed this app specifically for bank&rsquo;s corporate customers with maker &amp; checker login.	 	\n&bull;	 	Experience in 	publishing the app to Google Play store.	 	\n \n \nPERSONAL INFORMATION	 	\n 	\nDate of Birth	 	October 12, 1993	 	\nGender	 	Male	 	\nMarital Status	 	Married	 	\nNationality	 	India	 	\nLanguages Known	 	English, Tamil',2,2,1,'Recruiter','SL','N',14,1,'2024-07-22 08:46:28','2024-07-19 08:46:28',0,NULL,NULL,'2024-07-19 07:59:54',0),(413,100,73,35,NULL,'73_100_2022332361669a303361016_Bhushan_Bende_Developer_Connected_Platform_(1).pdf','Bhushan Bende_Developer_Connected Platform (1).pdf','Bhushan	Bende	\nSoftware	Engineer	\nRoles	:Senior	Software	Engineer	\nE-mail:	bhushanyahoo@gmail.com	\nMobile:	9405708908	\nProfessional	Synopsis:	\n	Experience	inPython	Developer	,Building	APIand	Data	Pipeline	,with	PRDomain	\n	Experience	onworking/building	Python-Fast	API	hosted	onLambda	connected	with	Elastic	Search	\n	Building	ETL	&amp;ELT	Data	Pipeline	inPython	with	AWS	Managed	Streaming	Application	Flink,	Self	Managed	Flink	and	AWS	Lambda	\n	5years	ofFunctional	and	Automation	Testing	experience	inInvestment	Banking	Domain	,Payment	Domain	and	Media	Monitoring.	\n	Keen	understanding	onCapital	Market	and	Money	Market.	\n	Well	versed	knowledge	ofPayments	system	and	Gateway	transaction	\n	Well	versed	inallphases	ofSoftware	Development	Life	Cycle	(SDLC)	and	Software	Testing	Life	Cycle	(STLC).	\n	Experience	inTesting	Web	Based	Application,	Windows	Based	Application	,Web-Service	and	Database	.	\n	Extensive	Knowledge	inwriting	Functional	and	Regression	Test-cases	,maintaining	and	modifying	test	cases.	\n	Proficient	inautomation	using	Selenium	Web-driver	with	Java	programming	language	.	\n	Extensive	knowledge	ofwriting	scripts	using	Element	locators	,Web-driver	methods	and	Java	Programming	.	\n	Generating	defect	report	and	interacting	with	developers	toresolve	the	defects.	\n	Having	good	exposure	todefect	management,	raising	defect/bugs.	\n	Having	good	time	management	towork	ondifferent	types	ofreleases	simultaneously	(BAU/OOC/Strategic).	\n	Has	rigorously	worked	onAPI	Testing	and	API	Test	Automation	with	JavaScript	-Mocha	Framework	\n	Extensive	knowledge	onvarious	testing	methodologies	like	UAT,	SIT,	Regression	testing,	Sanity	testing,	Smoke	testing,	and	\nFunctional	testing	.	\n	Experience	inusing	Test	Management	tool	HP-ALM,	JIRA	,Rally	.	\n	Great	exposure	toMS-OFFICE	suite	,Google	Suite	.	\n	Worked	extensively	onautomating	BFS	applications	from	enhancing	the	Automation	framework	inEclipse	using	Maven	tocommitting	\nthe	code	toBitBucket	/GITHUB	and	storing	test	artefacts	inJIRA	after	every	sprint	completion	.	\n	Have	working	experience	inCI/CD	model.	\n	Has	Good	Understanding	onEvent	Driven	System,	that	takes	data	asevent	process	it.	\n	Worked	onsetting	upKAFKA	and	KINESIS	for	Data	Stream	Processing	\n	Worked	onBuilding	Data	Pipeline	inPython	and	Deploying	itonAWS	Lambda	\n	Has	good	interpersonal,	communication	and	organizational	skill	with	ability	tointeract	effectively	with	colleagues	atalllevel.	\nTech	Stack:\nLanguages:	Python,	Core	Java,	SQL	,Javascript-NodeJS	\nOperating	System:	Windows,	Linux	,MacOS	\nTest	Management	Tool:	HP-ALM,	JIRA	,Rally	\nData	Base	Tools:	DBeaver,	MySQL,	Microsoft	SQL	Server	.,Google	Big	Query,	ElasticSearch	\nKnowledge	And	Worked	On:	API	Testing	with	POSTMAN,	API	Performance	Testing	with	JMeter	,ETL	Testing,	GCP,	AWS	EC2,	AWS	Lambda,	\nAWS	KINESIS,	Kafka,	Data	Streaming	Project,	API	Contract	Testing,	Docker	\nVersioning	Tool:	GIT	\nCertification:	GIT	(Udemy)	,AWS	API	GATEWAY(AWS),	GCP	KAFKA(GOOGLE),	Streaming	Pipeline	Using	Pyspark	and	Kakfa	\nAutomation	Framework	:BDD	-Cucumber,	TDD-	TestNG,	JavaScript	Mocha	\nAPI	Development	Framework	:Fast	API	Python	\nDeployment	Framework	:Zappa	\nAutomation	Tools:	Selenium	Web-driver,	Jenkins	,TestNG,	Cucumber	\nDeployment	Stack:	Kubernetes,	AWS	EKS	\nEmployment	Detail:	\nName	OfCompany	:Onclusive,	Mumbai	\nDesignation	:Senior	Software	Engineer	(Data	Engineer)	\nDuration	:19Oct	2022	ToCurrent	\nName	OfCompany	:TSYS	,Pune	\nDesignation	:Automation	Developer	\nDuration	:17May	2021	To	18Oct	2022	\nName	OfCompany	:Altimetrik,	Pune	\nDesignation	:Automation	Quality	Engineer	\nDuration	:18Jan	2021	To15May	2021	\nName	ofCompany	:Cognizant	Technology,	Pune	\nDesignation	:Test	Professional	\nDuration	:26June	2018	TO	15Jan	2021\n\nOnclusive	\nProject	1:	\nName	:Data	Platform	\nDesignation	:Senior	Software	Engineer	\nProject	Description	:Onclusive	isaMedia	Monitoring	Company.	Which,	monitors	the	Articles	,Content,	News,	Radio,	Youtube	and	also	TV	\nChannel.	Tofind	out	the	sentiment	for	the	particular	product	inmarket.	\nWhere	we	have	multiple	sources	who	pushes	Petabyte	ofdata	into	our	system	we	stream	that	into	MSK	Kafka	and	then	into	AWS	Kinesis	,using	\nData	Pipeline	asaconsumer	we	fetch	itand	process	ittoElastic	Search.(Elastic	stack)	\nAfterwards	this	used	for	sentiment	analysis	ofmarket	and	further,	using	API	this	Data	isfetch	and	given	toDown-Stream	Application	for	ML/AI	\npipeline.\nRole/Responsibility:	\n	ToDevelop	API	that	connects	with	Elastic-Search	and	fetch	out	data	with	relevant	query	that	we	build	run	time	.	\n	We	dohave	lots	ofAPI	that	exports	the	for	articles	based	onuser-query,	Iworked	onDeveloping	and	maintaining	API	\n	Todevelop	Data	Pipeline	Fetching	data	from	Apache	Kafka	and	AWS	Kinesis	then	transforming	and	Pushing	ittoStorage.(with	hosted	\nservice	onAWS	Lambda)	\n	Interm	tomodernize	these	pipeline	and	improving	the	performance	we	have	shifted	toApache	Flink	and	Apache	Beam	Pipeline	\no	Where	itfetch	data	from	Kafka,	using	UDF	we	modify	and	transform	this	data	and	push	itback	toElastic	Sink.	\no	Elastic	Sink	isself	managed	service	that	push	data	toElastic	Search	\n	Responsible	for	developing	and	deploying	the	API-P	oller	onAWS	ECS	tofetch	data	from	various	API	sources,	transform	it,and	store	it	\ninto	S3before	pushing	ittoKinesis	for	further	processing.	\nOther	Responsibilites	:	\n	Taking	part	inDesigning	Data	Pipeline	\n	Modernizing	Data	Pipelines	\no	Earlier	we	were	using	AWS	Kinesis	for	our	Data	Streaming,	now	we	have	designed	the	pipeline	touse	Kafka	which	has	good	\nRetention	period	and	also	tomaintain	the	AWS	Cost	itwill	becheaper	than	Kinesis.	\n	Adding	Layer	for	Open-Telemetry	intoServerless	AWS	Lambda	tocapture	telemetry	data	for	Failure	Analysis	\n	Deploying	StateFullset	ElasticSearch	InKubernetes	\n	Streaming	Data	Processing	On	Apache	Beam	Pipeline	\nProject	2:	\nName	:Data	Platform	\nDesignation	:Automation	Developer	\nProject	Description	:Onclusive	isaMedia	Monitoring	Company.	Which,	monitors	the	Articles	,Content,	News,	Radio,	Youtube	and	also	TV	\nChannel.	Tofind	out	the	sentiment	for	the	particular	product	inmarket.	\nWhere	we	have	multiple	sources	who	pushes	Petabyte	ofdata	into	our	system	we	stream	that	into	MSK	Kafka	and	then	into	AWS	Kinesis	,using	\nBroadcast	pipeline	asaconsumer	we	fetch	itand	process	ittoElasticSearch.	\nAfterwards	this	used	for	sentiment	analysis	ofmarket	and	further,	using	API	this	Data	isfetch	and	given	toDown-Stream	Application	for	ML/AI	\npipeline.\nRole/Responsibility:	Worked	asaAutomation	Architect	\n	ToDevelop	API	Automation	Framework	based	onNodeJS-JavaScript.	\n	Inclusion	ofBDD	inNode-JS	Framework.	\n	Maintaining	Automation	Framework	asaQA	Engineer	for	allthe	In-scope	API	\n	Completing	Testing	using	Manual	Testcases	and	Automation	\nOther	Responsibilites	:	\n	Taking	part	inDesigning	Data	Pipeline	\n	Modernizing	Data	Pipelines	\no	Earlier	we	were	using	AWS	Kinesis	for	our	Data	Streaming,	now	we	have	designed	the	pipeline	touse	Kafka	which	has	good	\nRetention	period	and	also	tomaintain	the	AWS	Cost	itwill	becheaper	than	Kinesis.	\n	Adding	Layer	for	Open-Telemetry	intoServerless	AWS	Lambda	tocapture	telemetry	data	for	Failure	Analysis\n\nTSYS	:Project	Worked	On	\nProject	1:	\nName	:UCP	-Global	Payments	\nDesignation	:Automation	Developer	\nProject	Description	:	\nAsaglobal	payments	we	wants	our	Merchants	has	full	capabilities	toselect	their	transaction	gateway	asper	their	own	\nconvenience	sothat	they	can	decide	their	security	level	and	transaction	changes	asper	area	they	are	serving	.Asaresult	we	have	created	aGlobal	\nPlatform	when	Global	Payment	will	board	Merchants	onUnified	Platform	(Unified	Commerce	Platform)	that	itwill	gives	right	toMerchants	toselect	\ngateway	without	any	code	changes.	\nHere	we	asaGlobal	Payments	will	take	care	ofinternal	transformation	and	Merchant	just	need	toplug	out	APIs	and	start	\nplaying	the	business	without	worrying	about	their	internal	transaction	transformation.	\nProject	2:	\nName	:Propay	Enterprise	Integration	\nDesignation	:Automation	Developer	\nProject	Description	:	\nAsTSYS,	AGlobal	Payments	Company,	we	act	asamediator	between	payment	gateways,	platform	and	Merchant.	\nMindBody	asaclient	comes	toGlobal	Payments	with	ask	that	they	want	tomigrate	their	allMerchant	toPROPAY	platform.	We	GPAPI	handles	their	\nTransaction,	Merchant	Boarding	Requirement	and	other	POS	devices	requirement	within	Single	Request.	\nArchitecture	works	like	take	abase	request	from	Merchant(Mindbody	Merchant)	segregate	and	convert	that	request	into	required	request	form	\nDownstream	I.ePROPAY,	TransIT,	SoundPayment,	PAX	Portal,	Express.	Receive	the	response	and	send	ittomerchant.	\nAsnumber	ofFinancial	entities	comes	into	picture	with	this	project,	ithas	avery	huge	scope.	Itincludes	payment	processing	and	merchant	\nprocessing.\nRole/Responsibility:	\n	Asbeing	aintegral	part	ofEnterprise	Integration	Team	my	responsibilities	were	related	togather	and	understand	the	latest	\nchanges/requirements	for	the	Gateway	Release	.	\n	Write	functional	and	regression	test	cases.	Used	POSTMAN	for	API	Testing	\n	Execute	manually	functional	and	regression	pack,	manage	test	cases	inRally	\n	Raise	defect	and	communicate	with	developers	toresolve	it.	\n	Reporting	Bugs,	verifying	logs,	verifying	server-side	services,	verifying	data,	generating	SQL	scripts	for	Database	changes.	\n	Verify	other	dependent	systems	are	receiving	proper	transaction	message	and	help	them	for	payments	booking	scenarios.	\n	Create	and	Execute	Automat	ion	Test	Scripts,	inNodeJS	based	framework	inJavaScript	.	\n	Verify	API-Integration	working	asexpected	\n	API	Performance	testing	with	JMete	r	\n	Generating	Sign-off	report	with	notifying	that	everything	isworking	fine	with	allnew	changes,	sothat	allnew	requirements	should	be	\nlive	inproduction	inupcoming	bi-weekly	release.	\n	Handling	multiple	developers	overseas.	\n	Communicating	with	Dev-ops	team	tocheck	new	build-changes,	DB-changes,	services	side	changes.	\n	Experience	inworking	with	Agile	and	CI/CD	testing	Models	.	\nAltimetrik	:Projet	Worked	On	:	\nProject	:	\nName	:CitiBank	-High	Value	Payments	\nDesignation	:Quality	Engineer	\nProject	Description	:	\nFew	High	Valued	Customers	are	tied	up	with	CitiBank,	they	transact	huge	amount	ofmoney	per	Business	Day.	We	have	\ncreated	DashBoards	tokeep	atrack	ontheir	transactions	and	check	and	observe	what	isthe	status	for	that	payment	and	transactions,	Inthe	form	\nonGraphs	and	Tabular	Data	we	are	displaying	data	.	\nRoles/Responsibility	:	\n	Write	Test	Cases	For	various	Dash-Board	showing	different	Types	ofData	Analysis	for	Payments.	\n	Analyse	Requirement	From	JIRA/BRD/FRD	\n	DB	Testing	with	high	number	ofHigh	Valued	Payment	with	Different	Types	OfMOP	(Mode	OfPayments)	\n	UITesting	-Verify	Data	Calculation	and	computation	for	the	Data	from	DB	iscorrect.	\n	Functional	And	Regression	Testing.	\n	Create	UIAutomation	Framework	Based	onJava	Selenium	BDD	-Cucumber.\n\nCognizant	:Project	Worked	On:	\nProject	1:	\nName	:Global	Markets	(Global	Securities	Lending)-	Trade	Execution	and	Management	(Short	&ndash;Selling)	\nClient:	Credit	Suisse	\nProject	Type	:Functional	And	Automation	Testing	\nDuration:	24Sept	2018	toJan	2021	\nProject	Description:	\nCredit	Suisse	uses	anapplication	toBook	trades	for	Short	Selling.	Where	Borrow,	Loan	and	Collateral	Trades	were	booked	\nevery	day,	this	isavery	heavy	application	asitisused	bynumber	ofusers	allover	the	Globe.	\nRole/Responsibility:	\n	Togather	and	understand	the	latest	changes/requirements	for	the	release	from	Business	Requirement	Documents.	\n	Write	functional	and	regression	test	cases.	\n	Execute	manually	functional	and	regression	pack,	manage	test	cases	inQuality	Centre	-HP	ALM	\n	Raise	defect	and	communicate	with	developers	toresolve	it.	\n	Reporting	Bugs,	verifying	logs,	verifying	server-side	services,	verifying	data,	generating	SQL	scripts	for	Database	changes.	\n	Verify	other	dependent	systems	are	receiving	proper	trade	message	and	help	them	for	trade	booking	scenarios.	\n	Create	and	Execute	Automated	Test	Scripts,	and	verifying	results.	\n	Verify	API-Integration	working	asexpected	\n	API	Performance	testing	with	JMete	r	\n	Generating	Sign-off	report	with	notifying	that	everything	isworking	fine	with	allnew	changes,	sothat	allnew	requirements	should	be	\nlive	inproduction	inupcoming	bi-weekly	release.	\n	Taken	initiative	toenhance	automation	ofWeb	Based	Application	using	Selenium	WebDriver	with	IDE	Eclipse	.		Handling	multiple	developers	overseas.	\n	Communicating	with	Dev-ops	team	tocheck	new	build-changes,	DB-changes,	services	side	changes.	\n	Experience	inworking	with	Agile	and	CI/CD	testing	Models	.	\nProject	2:	\nName	:Security	Financing	Transaction	Regulation	\nClient:	Credit	Suisse	\nProject	Type	:Functional	Testing	\nDuration:	Jan	2020	toJan	2021	\nProject	Description:	\nThis	isaglobal	change	where	every	Investment	Bank,	needs	toreport	their	allEurope	based	transaction,	tothe	certain	\nReporting	Entity.	\nRole/Responsibility:	\n	Togather	and	understand	the	change	from	Business	Requirement	Documents.	\n	Write	functional	and	regression	test	cases.	\n	Execute	manually	functional	and	regression	pack.	\n	Raise	defect	and	communicate	with	developers	toresolve	it.	\n	Verifying	logs,	verifying	server-side	services,	verifying	data,	generating	SQL	scripts	for	Database	testing.	\n	Verify	other	dependent	systems	are	receiving	proper	trade	message	and	help	them	for	trade	booking	scenarios.	\nProject	3:	\nName:	Fixed	Income	Securities	\nClient:	Credit	Suisse	\nProject	Type:	Functional	Testing/API	Testing	\nDuration:	March	2020	toMay	2020	\nProject	Description:	\nFixed	Income	securities	tobeimplemented	for	certain	Client/Counterparties	with	specified	entity	directions,	\nFor	that	combination	ofClient	and	Fixed	Income	Securities	system	calculating	rate	and	fetching	them	from	API	end	point.\n\nRole/Responsibility:	\n	Togather	and	understand	the	changes.	\n	Verify	API-Integration	working	asexpected.	\n	Write	and	execute	manual	and	regression	pack.	\n	Verify	logs,	DB	testing	reporting	defect	\n	Performance	testing	for	API	with	JMeter	.	\nAcademic	Details:	\nSr.No	Qualification	University/Board	Passing	Year	Percentage	\n1.	Executive	PDGM	MIT	SDE	2022	Grade	A	\n2.	Bachelor	inComputer	Application	Amravati	University	2017	68%	\n3.	HSC	Maharashtra	State	Board	2014	62.46%	\n4.	SSC	Maharashtra	State	Board	2012	82.55%	\nOther	Qualification	Details:	\nSerialNo.	Qualification	University	/Board	Passing	Year	Percentage	\n1.	Eng-Typing	30	Maharashtra	State	Council	ofExamination,	Pune	Dec-2012	65%	\n2.	Eng-Typing	40	Maharashtra	State	Council	ofExamination,	Pune	Dec-2012	51%	\n3.	MS-CIT	Maharashtra	State	Board	OfTechnical	Education,Mumbai	July-2010	92%	\nSelf-Learning	Project:\n	Project	1:Text	files	reader.	(Text	tospeech).	(Using	python	and	GTTS	library)		Project	2:QR	code	generator	(Using	Python)		Project	3:Speech	totext	(Using	Python	and	Google	Speech	Recognition	Library)	\nAchievement:	\n	Awarded	as&ldquo;Pillar	OfThe	Month	&ldquo;(Cognizant	May	2019)		Best	&ldquo;Student	ofYear	&rdquo;(CMCS	2017)	\nPersonal	Details:	\nName:	Bhushan	Ashokrao	Bende	Dob:	18/01/1996	Permanent	Address:	Old	Gilani	Nagar,	Yavatmal	445001	Maharashtra	Current	Address:	Venkat-Padmavati	PGbehind	Persistent,	Hinjewadi	,411057	Pune	Strength:	Good	Communication,	Quick	learner	Hobby:	Cooking,	Gardening	Languages	known:	English,	Marathi,	Hindi	\nMobile	+91	9405708908	\nEmail:	Primary:	bhushanyahoo@gmail.com	\nDeclaration:	Ihereby	declare	that	the	above	information	istrue	tothe	best	ofmy	knowledge	and	allnecessary	documents	will	beprovided	as&amp;	\nwhen	required.	\nSignature:	Bhushan	Ashokrao	Bende	\nDate:	Place:	Pune',2,2,1,'Recruiter','SL','N',14,1,'2024-07-22 09:33:25','2024-07-19 09:33:25',0,NULL,NULL,'2024-07-19 09:21:55',0),(414,101,73,38,NULL,'73_101_1436010278669b9464b85c2_HMI_Developer-Swapnil_Thamake.pdf','HMI Developer-Swapnil_Thamake.pdf','Sw ap nil  T h am ake  \nTe am  L e ad  \n                                             \nap plic a tio n i m pro ve m en ts . C urre n t p ro je ct w ork in g  o n F o rd  C re d it  ( ﬁ nte ch ) a n d  L a st p ro je ct w as w ork e d\n                                       \nsw ap 3848@ gm ail .c o m  \n\nPune, I n d ia  \nlin ke d in .c o m /in /s w ap nil - t h am ake -2 14314a0  \nWORK  E X PER IE N CE \nTe am  L e ad  \nTe ch  M ah in d ra  \n04/2 022 -  P re se n t\n, \n  Pun e \nFo rd  C re d it  A pplic a tio n : ( F in te ch ) \nLe ad in g  t o  m y 2  b ud dy&#039;s  a n d  d eve lo pin g  M obil e  A pplic a tio n\nis  b ase d  o n ﬁ nan cia l a n d  a cco unt m an ag em en t s e rv ic e s \nUse  s k il ls  C ode o ptim iz a tio n, A pplic a tio n s e cu rit y , d esig n\npatte rn s a n d  r e so lv e  l o w  p erfo rm an ce  i s su es. \n                 \n           \nAsso cia te  C onsu lt a n t \nCap gem in i I n d ia  \n02/2 020 -  0 4/2 022\n, \n  Pun e \nHSB C B an k I n dia  \nWork e d  o n F in an ce  D om ain  &amp;  B an kin g  d o m ain  a p plic a tio n.\nIm ple m en te d  s o rt in g , s e arc h in g  a lg o rit h m s a n d  d esig n\npatte rn s. \n                         \nQR c o de, T ra n sa ctio n h is to ry , R ais e  d is p ute . \nAnd ro id , K otlin , R xJav a , D ag ger 2 ,M VP, R etro ﬁ t, J e n kin s,\n                    \nConte xt, H ooks. \nApplic a tio n E ng in eer -  M obil e  A pplic a tio n\nDeve lo per \nRaw  E n g in eerin g  I n d . P vt. L td . \n08/2 018 -  0 1/2 020\n, \n  Vir a r -  M um bai \nIn  R aw  E ngin eerin g i  h ave  w ork e d  o n s p o rt s  d o m ain  p ro je cts  \n                   \n                             \nso rt in g  a n d  s e arc h in g  a lg o rit h m s. \n                 \nRetro ﬁ t, J e n kin s, G it , A W S, X M PP,F ir e b ase , C ra sh ly tic s \nMobil e  A pplic a tio n D eve lo per \nG.N .R eatil  S e rv ic e s P vt. L td .( Z ap m art .c o m ) \n05/2 016 -  0 7/2 018\n, \n  Mum bai \nZa p m art  i s  O nlin e G ro ce ry  P ro du ct B ase d  C om pan y \nReq uir e m en t &amp;  A naly sis , D esig n a n d  D eve lo p A pplic a tio ns,\nWrit e  A PI&#039;s , W ork  o n B ack -e n d  a n d  D B. \nWork e d  o n z a p m art  a p plic a tio n w hic h  b ase d  o n o nlin e\ngro ce ry  s to re . D eve lo ped  o rd er t ra ck in g  f u nctio nalit y  u sin g\nGoogle  M ap . \nAnd ro id , j a v a , V olle y, G it , B it b uck e t, S Q LLit e , F ir e b ase ,\nCra sh ly tic s. \nSK IL L S  \nAnd ro id  S D K \nJav a  \nGit h u b  \nSQ Lit e  \nRest A PI \nMyS Q L \nFir e b ase  \nRxJav a  \nSo narQ  \nAW S \nMVC \nMVVM  \nRO OM  \nJIR A  \nMock it o  \nSyste m  D esig n \nKotlin  \nMult iT h re ad in g  \nJe tp ack  \n \n    \nAlg o rit h m s \nRetro ﬁ t \nData  s tru ctu re  \nFir e b ase  \nRxJav a  \nLiv e d ata  \nReact N ativ e  \nTyp esc rip t \n    \n \n \n  \nPR O FE SSIO NAL P R O JE C TS \nFo rd  C re d it\n ( 0 4/2 022 -  P re se n t)\n \nHSB C b an kin g\n ( 0 3/2 020 -  0 4/2 022)\n \nHaw ks A re n a F a rm  S ta te  &amp;  S acra m en to  K in g &#039;s\n ( 0 1/2 019 -  0 1/2 020)\n \nJD  P ow er\n ( 0 8/2 018 -  1 2/2 018)\n \nMed ic a lw ale  D octo r A pp\n ( 0 3/2 018 -  0 7/2 018)\n \nza p m art .c o m\n ( 0 8/2 016 -  0 1/2 018)\n \nED UCATIO N \nMCA  ( M aste r i n  C om pute r A pplic a tio n) \nKIT &#039;S  I M ER  K olh ap ur \n07/2 013 -  0 6/2 016\n, \n  Kolh ap u r,M ah ara sh tra , I n dia  \nBCA  \nGopal K ris h n a G okh ale  C olle g e K olh ap ur \n06/2 009 -  0 4/2 012\n, \n  Kolh ap u r ,  M ah ara sh tra  ,  I n dia  \n12th  \nS.M .L o hia  C olle g e, K olh ap ur \n06/2 007 -  0 2/2 009\n, \n  Kolh ap u r \n10th  \nJyo tir lin g  v id ya m an d ir  V ad an ag e \n06/2 002 -  0 3/2 007\n, \n  Kolh ap u r \nIN TER EST S \nPho to gra p hy \nSin g in g  \nCric k e t \nAch ie ve m en ts /T a sk s \nAch ie ve m en ts /T a sk s \nAch ie ve m en ts /T a sk s \nAch ie ve m en ts /T a sk s 	\nUnitTest	\nMVP\nAOSP	\nSplunk	CSS	  Jacoco	\n8369096660	\nMobile Application Developer with 8 years of experience in coding, team lead, architecture design and\non HSBC Bank on financial domain . I have also worked on Sports, Health Care, Automative and E-Commerce.	\nReact Native, Typescript, Android, Kotlin, MVVM,\nJWT, Jetpack, Coroutine, Git, Splunk\nDeveloped application with key features like money transfer,\nNexus, SonarQ, Git, SSL pinning, React Native,\nDeveloped application for NBA teams and Automative App\nLive score update with xmpp protocol. Used multithreading,\nAndroid, Java, Kotlin, RxJava, Dagger2, MVP, AOSP,',2,2,1,'Recruiter','SL','N',14,1,'2024-07-23 10:45:06','2024-07-20 10:45:06',0,NULL,NULL,'2024-07-20 10:41:40',0),(415,102,76,NULL,NULL,'76_102_175841139669d1c38ece8c_Aashi-Resume_(1).pdf','Aashi-Resume (1).pdf','OBJE C TIV ES	\nSkil le d  S o ft w are  D eve lo p er w it h  2 +ye a rs  o f\nh an d s-o n   exp erie n ce   in   ChatB ot\nD eve lo p m en t  .S eekin g a  c h alle n ge  ro le  to\na p ply  m y e xp ertis e  in  cre a tin g in nova tiv e\nso ft w are  s o lu tio n s a n d  c o lla b ora tin g w it h  a\nt a le n te d  d eve lo p m en t t e a m .	\nSK IL LS	\nP R O GRAM MIN G S K IL LS\nN od e J S  ,  M ic ro so ft  B ot F ra m ew ork  ,	 	\nJ a va S crip t\nTO OLS\nV is u al S tu d io  C od e ,  E m ula to r ,  M on go d b	 	\nC om pass	\nC ER TIF IC AT ES	\nKore .A I B ot b asic  a n d  a d va n ce d .\nO pen  A I A P I a n d  C H AT  G PT f o r d eve lo p ers	 	\nb y U dem y.	\nE D U CAT IO N	\nB.T e ch  	, E le ctr ic a l 	,  8 .0 1 	cg p a, 	\nM adha v 	In stit u te  	O f 	T e ch n olo g y 	&amp;  	Scie n ce	\n0 7/2 017 &ndash;  0 6/2 021	    	\nG w alio r, M ad hya  P ra d esh\n1 2th  	,8 3.8 % , 	N atio n a l 	C hil d re n  	H ig h 	\nSeco n d a ry  	S ch ool,B ada ga on	\n2017	 |  G w alio r, M ad hya  P ra d esh	\nP R O FE SS IO NAL E X P ER IE N CE	\nSYS T EM  	EN GIN EER  	, T AT A  	CO NSU LT A N CY 	\nS ER VIC ES (T C S )\n06/2 021 &ndash;  p re se n t	 |  D elh i,	 I n d ia	\n&bull;I  h ave  g a in ed  e xp erie n ce  i n  c h atb ot d eve lo p m en t	 	\nacro ss N LU  p la tfo rm s l ik e  M ic ro so ft  B ot F ra m ew ork	 	\ns d k f o r v a rio u s c h an nels  l ik e  m s t e a m s ,  d ir e ctlin e	 	\nusin g N od e j s  .\n&bull;P ro fic ie n t i n  b uil d in g R EST fu l A P Is  a n d  w ork in g w it h	 	\nM on go D B t o  m an age  a n d  q uery  d ata . A d ep t a t	 	\nco lla b ora tin g w it h  c ro ss -fu n ctio n al t e a m s t o  d eliv e r	 	\nh ig h -q ualit y  s o ft w are  s o lu tio n s u sin g N od e j s . I  h ave	 	\nw ork  w it h  d if fe re n t t e a m s t o  d eliv e r h ig h  q ualit y	 	\np ro je cts  a n d  c o n tr ib ute d  t o  a  p osit iv e  t e a m	 	\nen vir o n m en t b y p ro vid in g g u id an ce  a n d  s u p port t o	 	\nte a m  m em bers .\n&bull;U til iz e d  a n d  I n te g ra te d  Q nA M ake r/ L a n gu age  S tu d io	 	\ni n to  a  c u sto m  c h atb ot a p plic a tio n  t o  c re a te  a	 	\nK now le d ge  b ase  o f f r e q uen tly  a sk e d  q uestio n s,	 	\nim pro vin g r e sp on se  t im es ,  a cc u ra cy  a n d  e n ab lin g	 	\nnatu ra l l a n gu age  i n te ra ctio n s w it h  u se rs .\n&bull;In te g ra te d  t h e c h atb ot w it h  o th er s y ste m s, d ata b ase s	 	\na n d  A P Is  t o  a cce ss a n d  r e tr ie ve  r e le va n t i n fo rm atio n .\n&bull;In te g ra te d  L U IS /C LU  f o r i n te n t r e co gn it io n  a n d  e n tit y	 	\ne xtra ctio n  ,  e n han cin g t h e c h atb ot	 	\nu n d ers ta n d  a n d  u se r q uerie s.\n&bull;P ro fic ie n t i n  d esig n in g, i m ple m en tin g, a n d	 	\ncu sto m iz in g A d ap tiv e  C ard s f o r c h atb ot a p plic a tio n s.\n&bull;L e d  t h e i n te g ra tio n  a n d  c u sto m iz a tio n  o f M ic ro so ft	 	\nr e a l- t im e t ra n sla tio n  s e rv ic e s w it h in  c h atb ot t o	 	\nfa cil it a te  s e a m le ss m ult il in gu al c o m mun ic a tio n .\n&bull;In te g ra te d  G PT s e rv ic e  f o r m ult ip le  u se ca se s w it h  t h e	 	\nhelp  o f m ult ip le  a zu re  s e rv ic e s l ik e  a zu re  c o gn it iv e	 	\nse a rc h , a zu re  b lo b  s to ra ge  ,  a zu re  o p en  A I :	\na . T e xt m od el &ndash; G PT t u rb o 3 .5  ,\nb . E m bed din gs m od el - E m bed din gs a d a002.	\n&bull;I s u cce ssfu lly  l e d  t h e i m ple m en ta tio n  o f a  c u tt in g-e d ge	 	\np ro je ct l e ve ra gin g O pen AI t e ch n olo gie s, f o cu sin g o n	 	\ntw o k e y f u n ctio n alit ie s:	\na . F e tc h in g d ata  f r o m  d ocu m en ts .\nb . S u m mariz a tio n  o f u se r q uery .	\nR EW AR D S &amp;  R ECO GN IZ AT IO N	\nRece iv e d  	Xce le ra te  	W arrio r 	C ertif ic a te .	\nR ece iv e d  	Fe ed back  	En th usia st 	C ertif ic a te .	\nA ash i G upta\nD eve lo p er	\naash ig u pta 2300@ gm ail .c o m\n7000606303\nG w alio r\n23/1 0/2 000\nh tt p s:/ /w ww.l in ke d in .c o m /in /a ash i-\ng u pta -6 a 573619b',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 14:33:28',0),(416,102,76,NULL,NULL,'76_102_599246470669d1c38ed432_Aashi-Resume.pdf','Aashi-Resume.pdf','OBJE C TIV ES	\nSkil le d  S o ft w are  D eve lo p er w it h  2 +ye a rs  o f\nh an d s-o n   exp erie n ce   in   ChatB ot\nD eve lo p m en t  .S eekin g a  c h alle n ge  ro le  to\na p ply  m y e xp ertis e  in  cre a tin g in nova tiv e\nso ft w are  s o lu tio n s a n d  c o lla b ora tin g w it h  a\nt a le n te d  d eve lo p m en t t e a m .	\nSK IL LS	\nP R O GRAM MIN G S K IL LS\nN od e J S  ,  M ic ro so ft  B ot F ra m ew ork  ,	 	\nJ a va S crip t\nTO OLS\nV is u al S tu d io  C od e ,  E m ula to r ,  M on go d b	 	\nC om pass	\nC ER TIF IC AT ES	\nKore .A I B ot b asic  a n d  a d va n ce d .\nO pen  A I A P I a n d  C H AT  G PT f o r d eve lo p ers	 	\nb y U dem y.	\nE D U CAT IO N	\nB.T e ch  	, E le ctr ic a l 	,  8 .0 1 	cg p a, 	\nM adha v 	In stit u te  	O f 	T e ch n olo g y 	&amp;  	Scie n ce	\n0 7/2 017 &ndash;  0 6/2 021	    	\nG w alio r, M ad hya  P ra d esh\n1 2th  	,8 3.8 % , 	N atio n a l 	C hil d re n  	H ig h 	\nSeco n d a ry  	S ch ool,B ada ga on	\n2017	 |  G w alio r, M ad hya  P ra d esh	\nP R O FE SS IO NAL E X P ER IE N CE	\nSYS T EM  	EN GIN EER  	, T AT A  	CO NSU LT A N CY 	\nS ER VIC ES (T C S )\n06/2 021 &ndash;  p re se n t	 |  D elh i,	 I n d ia	\n&bull;I  h ave  g a in ed  e xp erie n ce  i n  c h atb ot d eve lo p m en t	 	\nacro ss N LU  p la tfo rm s l ik e  M ic ro so ft  B ot F ra m ew ork	 	\ns d k f o r v a rio u s c h an nels  l ik e  m s t e a m s ,  d ir e ctlin e	 	\nusin g N od e j s  .\n&bull;P ro fic ie n t i n  b uil d in g R EST fu l A P Is  a n d  w ork in g w it h	 	\nM on go D B t o  m an age  a n d  q uery  d ata . A d ep t a t	 	\nco lla b ora tin g w it h  c ro ss -fu n ctio n al t e a m s t o  d eliv e r	 	\nh ig h -q ualit y  s o ft w are  s o lu tio n s u sin g N od e j s . I  h ave	 	\nw ork  w it h  d if fe re n t t e a m s t o  d eliv e r h ig h  q ualit y	 	\np ro je cts  a n d  c o n tr ib ute d  t o  a  p osit iv e  t e a m	 	\nen vir o n m en t b y p ro vid in g g u id an ce  a n d  s u p port t o	 	\nte a m  m em bers .\n&bull;U til iz e d  a n d  I n te g ra te d  Q nA M ake r/ L a n gu age  S tu d io	 	\ni n to  a  c u sto m  c h atb ot a p plic a tio n  t o  c re a te  a	 	\nK now le d ge  b ase  o f f r e q uen tly  a sk e d  q uestio n s,	 	\nim pro vin g r e sp on se  t im es ,  a cc u ra cy  a n d  e n ab lin g	 	\nnatu ra l l a n gu age  i n te ra ctio n s w it h  u se rs .\n&bull;In te g ra te d  t h e c h atb ot w it h  o th er s y ste m s, d ata b ase s	 	\na n d  A P Is  t o  a cce ss a n d  r e tr ie ve  r e le va n t i n fo rm atio n .\n&bull;In te g ra te d  L U IS /C LU  f o r i n te n t r e co gn it io n  a n d  e n tit y	 	\ne xtra ctio n  ,  e n han cin g t h e c h atb ot	 	\nu n d ers ta n d  a n d  u se r q uerie s.\n&bull;P ro fic ie n t i n  d esig n in g, i m ple m en tin g, a n d	 	\ncu sto m iz in g A d ap tiv e  C ard s f o r c h atb ot a p plic a tio n s.\n&bull;L e d  t h e i n te g ra tio n  a n d  c u sto m iz a tio n  o f M ic ro so ft	 	\nr e a l- t im e t ra n sla tio n  s e rv ic e s w it h in  c h atb ot t o	 	\nfa cil it a te  s e a m le ss m ult il in gu al c o m mun ic a tio n .\n&bull;In te g ra te d  G PT s e rv ic e  f o r m ult ip le  u se ca se s w it h  t h e	 	\nhelp  o f m ult ip le  a zu re  s e rv ic e s l ik e  a zu re  c o gn it iv e	 	\nse a rc h , a zu re  b lo b  s to ra ge  ,  a zu re  o p en  A I :	\na . T e xt m od el &ndash; G PT t u rb o 3 .5  ,\nb . E m bed din gs m od el - E m bed din gs a d a002.	\n&bull;I s u cce ssfu lly  l e d  t h e i m ple m en ta tio n  o f a  c u tt in g-e d ge	 	\np ro je ct l e ve ra gin g O pen AI t e ch n olo gie s, f o cu sin g o n	 	\ntw o k e y f u n ctio n alit ie s:	\na . F e tc h in g d ata  f r o m  d ocu m en ts .\nb . S u m mariz a tio n  o f u se r q uery .	\nR EW AR D S &amp;  R ECO GN IZ AT IO N	\nRece iv e d  	Xce le ra te  	W arrio r 	C ertif ic a te .	\nR ece iv e d  	Fe ed back  	En th usia st 	C ertif ic a te .	\nA ash i G upta\nD eve lo p er	\naash ig u pta 2300@ gm ail .c o m\n7000606303\nG w alio r\n23/1 0/2 000\nh tt p s:/ /w ww.l in ke d in .c o m /in /a ash i-\ng u pta -6 a 573619b',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 14:33:28',0),(417,102,76,NULL,NULL,'76_102_1279076832669d1c38ed5d2_Abhishek_Ankush_Resume__3.pdf','Abhishek_Ankush_Resume_ 3.pdf','',2,3,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 14:33:28',0),(418,102,76,NULL,NULL,'76_102_1891804581669d1c38ed947_ABHISHEK_CV_(1).pdf','ABHISHEK_CV (1).pdf','Abhishek Chaturvedi\n+91-8962547434 |reach2abhishekchaturvedi@gmail.com |\nlinkedin.com/in/abhi0642/ |\nhttps://github.com/abhi0642 Technical Skills\nLanguages\n: Python, Core Java, JavaScript, SQL, HTML/CSS\nFrameworks : Django, Flask, Torch for NLP, AWS SDK\nLibraries : Langchain, Openai, HuggingFace, Sentence-transformers, FAISS\nDatabases : Mysql, Postgresql, Chromadb, pgvector, Elastic vector database\nDeveloper Tools : PyCharm, Git, Visual Studio, Postman, Jupyter-Notebook, Google Colab\nOther : AWS, Docker, Natural Language Processing, Generative AI\nExperience Senior Software Developer - Indegene\nAug 2023 &ndash; Present\nBengaluru\n&bull; Led a team to integrate a product recommendation system with customer feedback, leveraging the Langchain\nframework with databases such as ChromaDB and PGVector for optimal performance.\n&bull; Designed and enhanced a responsive chatbot using React, langchain, and OpenAI, achieving a 92 accuracy rate\nthrough iterative prompt training. This versatile chatbot handles diverse data inputs &ndash; documents, databases,\nURLs, presentations, and images &ndash; transforming customer engagement.\n&bull; Implemented a large-scale enterprise search solution using AWS Kendra, optimizing search capabilities across\n1,000+ PDFs for enhanced data retrieval and user experience.\n&bull; Tailored chatbot solutions to align with brand strategies, ensuring robust security and the highest standards of data\nprotection.\nSenior Software Engineer - Apisero Mar 2022 &ndash; July 2023\nBangalore\n&bull; Designed and deployed a serverless API on AWS Lambda, using python events for seamless event-driven\nfunctionality. Integrated with AWS gateway API to provide scalable and efficient endpoints, optimizing cloud\nresource usage.\n&bull; Successfully automated Salesforce, Guidewire, and Symbility integrations through the use of the MuleSoft API and\nachieved a 50% reduction in data updation time.\n&bull; Successfully implemented CI/CD processes using GitHub flows on the MuleSoft anypoint platform. This enabled\nstreamlined and automated code deployment workflows, enhancing development efficiency and improving pro ject\ndelivery.\n&bull; Collaborated with multiple stakeholders to gather their requirements, led the end-to-end design of the integration\narchitecture, and conducted thorough testing across various scenarios.\nSoftware Developer - ICICI Lombard GIC Ltd. Aug 2020 &ndash; Feb 2022\nMumbai\nProduct Prediction Model\n&bull; Developed and Trained Machine Learning generic model using Regression model Techniques.\n&bull; Determined optimal strategies to understand the customer behavior during product launch and facilitate the\ntargeted market campaigns.\n&bull; Worked on a targeted algorithm to acquire an increase in market share by developing a customer segmentation\nalgorithm.\nSMS Engine(Samadhaan)\n&bull; Developed and designed an engine from scratch for customer communication via applications and upgraded our\ncentralized ILESB services for hassle-free integration.\n&bull; Finalised business requirement and delivered the engine within the minimum time possible using technologies like\nDjango and PostgreSQL.\n&bull; The engine improved the overall performance ratio from 98.8% to 99.4% and also decreased the turnaround time for\nmessage delivery.\n\nEducation\nNational Institute Of Technology Karnataka\nSurathkal\nMaster of Computer Application. July,2017 &ndash; May 2020\nWelingkar Institute of Management Development and Research Mumbai\nPGDM (Finance) July,2020 &ndash; May 2022\nMahara ja Ranjit Singh Indore\nBachelor of Computer Application. July,2014 &ndash; May 2017\nCertifications 1. Certified ScrumMaster\n&reg;(ScrumAlliance) Feb,2020 &ndash; Feb,2024\nCertificate ID: 001046716\n2. Machine Learning (Coursera) https://www.coursera.org/account/accomplishments/certificate/RXZPRNTHBK9X\nAchievement and Extracurricular activities &lowast;\nSecured AIR-80 in NIMCET-2017.\n&lowast; Competed at the state level in Muay Thai martial arts, demonstrating discipline, resilience, and a commitment to\npersonal growth and physical fitness.\n&lowast; Management Team Head in IGNITE (2018) cultural fest.\n&lowast; Secured AIR-65 in BIT-2017 MCA Entrance exam.',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 14:33:28',0),(419,102,76,NULL,NULL,'76_102_1493816248669d1c38edb57_Adhihariharan_Resume_.pdf','Adhihariharan Resume .pdf','The S park s F oundatio nD ata  S cie n ce a nd B usin ess A naly tic s I n te rn	C onducte d  a  P yth on-b ase d  d ata  a n aly sis  p ro je ct t o  p ro vid e v alu ab le  i n sig h ts  f or t arg ete d  a d vertis e m en ts  i n  t he I ndia n  P re m ie r L ea gu e\nto urn am en t. P re se n te d  f in din gs a n d r e co m men datio ns c o ncis e ly , 	e ffe ctiv ely	 c o m munic a tin g	 co m ple x	 d ata  f o r s ta k eh old er d ecis io n-m ak in g.	\nO DIN  A IJunio r A I E ngin eer	 	S pea rh ea d ed  t h e d ev elo pm en t o f 	R AG	 eva lu atio n	 e n dpoin ts  i n  	F astA PI	, l e v era gin g 	L angC hain	 t o  e n su re  r o bust m etr ic  e sta b lis h m en t f o r	\nev alu atin g t h e R AG  p ip elin e&#039;s  p erfo rm an ce. R esu lt in g i n  a  n ota b le  	2 8%  i n cre a se  i n  a nsw er c o rre ctn ess	 f o r m ult i- a gen t c h atb ots  i n te g ra te d	\nw it h  O D IN  A I&#039;s  p la tfo rm s.\nO rc h estr a te d  t h e i n te g ra tio n o f e n dpoin ts  w it h in  t h e 	F astA PI	 b ack en d t o  s e a m le ssly  a u to m ate  t a sk s a n d o ptim iz e w ork flo w  p ro cesse s a cro ss	\nR ea ctJ S , A ngula rJ S , a nd N ex t.js  	fr o nte n d f r a m ew ork s. T his  i n clu ded  d ev elo pin g a  L in ked In  a u to m atio n f ea tu re  c ap ab le  o f e x tr a ctin g	\npro sp ect d ata  b ase d  o n j o b r o le s o r e x p erie n ce, a m ong o th er f u nctio nalit ie s, a nd lev era g in g	 S erp A PI.	 W hic h  e n han ced  t h e	 H R t e a m &rsquo;s	\np ro ductiv it y  b y s tr e a m lin in g j o b p ro sp ect s e a rc h in g b y	 7 0% .	 	\nPla yed  a  p iv o ta l r o le  i n  o rc h estr a tin g t h e s e a m le ss 	m ig ra tio n	 o f O D IN  A I&#039;s  d ata b ase  f r o m  	Fir e b ase	 ( N oSQ L) t o  	S upabase	 ( P ostg re S Q L),	\nd ev is in g a n d e x ecu tin g a  s tr u ctu re d  m ig ra tio n p la n  e nco m passin g o ver 	1 00	 en dpoin ts	 a n d o ver 	1 0,0 00 d ocu m en ts	 w hile  l e v era gin g 	para lle l	\nco lle ctio n t r a n sfe r t e ch niq ues t o  e x p ed it e  m ig ra tio n s p eed  a n d m in im iz e d ow ntim e.\nP ro vid ed  e sse n tia l t e ch nic a l s u pport i n  r e so lv in g b ug f ix es a n d u se r i n quir ie s, c olla b ora tin g c lo se ly  w it h  t he s upport t ea m  to t ro uble sh oot\nis su es w it h in  t h e F astA PI b ack en d, u tiliz in g 	D ock er c o nta in ers, N ew R elic , a nd S en tr y .io	 e rro r m onit o rin g t o  p ro m ptly  a d dre ss a n d 	deb ug	\nte ch nic a l i s su es.\nC olla b ora te d  s e a m le ssly  w it h  f r o nt-e n d t e a m  m em bers t o  e n su re  c ohesiv e d ev elo pm en t a nd d eliv ery  o f r obust A I-d riv en  s olu tio ns, u tiliz in g\nA gile	 m eth odolo gie s	. S pea rh ea d ed  t h e i m ple m en ta tio n o f v ario us f e a tu re s i n clu din g 	a udio  t r a nsc rip tio n, c h at t it le  g en era tio n, a nd a n A I	\ncla ssif ic a tio n t o ol,	 a m ong o th er s ig n if ic a n t c o ntr ib utio ns.	\nO ct 2 023 -  P re se n t	A ug 2 021 -  S ep t 2 021	+91 9790850675 &middot; 	adhi15au@gmail.com	 &middot; Linkedin	 - Github	 - Website	\nA D HIH ARIH ARA N  A U	\nPR OFESSIO N AL E X PE R IE N CE	Glo sy s T ech nolo gy S olu tio ns P vt. L td .T ra in ee	C om ple te d  a  c o m pre h en siv e 	fo ur-m onth  D ata  s c ie n ce c o urse , g ain in g h ands-o n e x perie n ce 	a n d e x p ertis e  i n  v ario us a lg o rit h m s a n d c o ncep ts .	\nC olla b ora te d  w it h  m y m en to r t o  s o lv e r e a l- w orld  p ro ble m s b y e m plo yin g a dva n ced  P yth on l ib ra rie s a n d f ra m ew ork s.	\nJ une 2 019 -  S ep t 2 019	S in gapore  M anagem en t U niv ersit y , 	S in gap ore	\n0 8/2 022 -  0 8/2 023\nM S: C om putin g, D ata  S cie n ce &amp;  E ngin eerin g r e se a rc h  t ra ck	E D UCA TIO N	Sri V en k ate sw ara  C olle g e O f E ngin eerin g, 	T am il N ad u	\n04/2 018 -  0 6/2 022\nB E: 	C om pute r S cie n ce a n d E ngin eerin g	L EA D ER SH IP  E X PE R IE N CE	FO RU M  O F E C O NO M IC  S T U DIE S B Y  E N GIN EER S	V ic e P re sid en t	, F O RESE  ( A pril 2 021 -  A pril 2 022)	\nE xecu tiv e	 D ir e cto r	, F O RESE  ( A pril 2 020 -  A pril 2 021).	 	\nC onducte d  m ock  p la cem en ts  (  O N LIN E |  O FFLIN E )  f or\npre -fin al y ea r s tu den ts  i n vit in g o ver 	1 00 C om pany H Rs.	\nL ed  a  	te a m  o f o ver 8 0 m em bers	 i n  t h e c lu b.	\n IE EE S SC I 2 022	S tu den t H elp er, I E EE S SC I 2 022 ( D ec 2 022).\nH elp ed  o rg an is e  t h e I E EE S SC I c o nfe re n ce.	\nY O UTH  R ED  C RO SS	 M em ber, Y outh  R ed  C ro ss ( A pril 2 020 -  A ugu st 2 022).	P R OJE C TS	Dete ctio n o f M alic io usly  T am pere d  L ung C ancer C T sca n s u sin g A nom aly  D ete ctio n.	A  h ig h  r is k  o f m ed ic a l i m ages b ein g h ack ed  a n d t a m pere d  w it h  i s  p ro bab le . T his  c a n  p ose  a  t hre a t t o t h e p ublic  a nd t he m ed ic a l i n dustr y .\nT his  p ro je ct i d en tif ie s t h is  p ro ble m  a n d p ro vid es a  w ay t o d ete ct t a m pere d  m ed ic a l i m ag es u sin g a nom aly  d ete ctio n w it h  a n a ccu ra cy  o f\n92.1 2%\nE m plo yed  	G AN s	 t o  s im ula te  t a m pere d  m ed ic a l i m ages, e n han cin g a n om aly  d ete ctio n f or a ccu ra te  i d en tif ic a tio n. E xp ertis e  d em onstr a te d  in	\nin te g ra tin g 	G AN s	 w it h  	T en so rflo w	 a n d 	P yT orc h	 f o r r o bust t a m perin g d ete ctio n i n  m ed ic a l i m agery .	\nS m art T ra ffic  C ontr o l S yste m .	In  r e sp onse  t o  I n dia &#039;s  p erv asiv e t r a ffic  i s su es, I  s p ea rh ea d ed  a  p ro je ct f ocu se d  o n a ddre ssin g t ra ffic  c ongestio n t hro ugh  in novativ e t ech nolo gy\nso lu tio ns. L ev era gin g m y e x p ertis e  i n  	C om pute r V is io n a nd I m age P ro cessin g	, 	\nI l e d  t h e d ev elo pm en t o f a  d yn am ic  t r a ffic  l ig h t s y ste m . B y e m plo yin g a dva n ced  t ech niq ues s u ch  a s 	O pen C V a nd Y O LO v3	, w e a ch ie v ed  a	\nr e m ark ab le  	9 5%	 a ccu ra cy  i n  r e a l- tim e t r a ffic  d ete ctio n w it h in  s p ecif ic  l an es.	 	\nA ddit io nally , m y r o le  e n co m passe d  s ig n if ic a n t c o ntr ib utio ns t o  a lg o rit h m ic  r efin em en t, e nsu rin g t he s e a m le ss i n te g ra tio n o f m ach in e\nle a rn in g p rin cip le s i n to  o ur s o lu tio n, u lt im ate ly  e n han cin g t ra ffic  m an ag em en t a nd d aily  c om mutin g e xp erie n ces.	\nP yth on &bull;  J a va &bull;  H TM L a n d C SS	  S Q L &bull;  C ++	  	\n&bull;  R ea ctJ S	  &bull;  B ash  &bull;  L in ux	  	\nF astA PI &bull;  F ir e b ase  &bull;  T ab le a u	  P ow er B I &bull;	\nE xcel &bull;  S upab ase  &bull;  G it	 	\nT en so rflo w  &bull;  O pen C V &bull;  P yT orc h  s c ik it -le a rn  &bull;	\nL an gC hain  &bull;  L am aIn dex	S K IL LS',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 14:33:28',0),(420,102,76,NULL,NULL,'76_102_1000849870669d1c38edce9_AmanResume.pdf','AmanResume.pdf','SU M M AR Y	PR O FE SSIO NAL E X PE R IE N CEED U CAT IO NSK IL LSC ER T IF IC AT IO NS	li n ke d in .c o m /in / am an ja in 10 1	\nam an ja in 78 1@ gm ail.c o m	+9 1- 8 0 573 3339 3	\nA M AN  J A IN\nM ac h in e L earn in g E ngin eer	\nH ig h ly  m otiv a te d  a n d p ro fe ssio n al M ac h in e l e arn in g E ngin eer w it h  o ve r 4  y e ars  o f e xp erie n ce d  w it h  a\nd em on str a te d  h is to ry  o f w ork in g i n  t h e i n fo rm atio n  t e ch n olo gy a n d p ro d uct i n dustr y .  S kille d  i n  D ata	 	\nS cie n ce ,  C om pute r V is io n ,  P yth on ,  S ta ts ,  N LP ,  S Q L a n d G en era tiv e  A I.  S tr o n g e n gin eerin g p ro fe ssio n al	 	\nw it h  a  B ac h elo r o f T ech n olo gy-B T ech  f o cu se d  o n  C om pute r S cie n ce  &amp;  E ngin eerin g.	\nLok su n  T ech no lo g ie s,  B eng alu ru	 	\nM an ufa ctu rin g D efe cts  &amp;  Q A I n sp ectio n  w it h  S im il a rit y  b ase d\nalg orit h m .( P ate n t i n  p ro g re ss:  U S)\nD esig n ,  d eve lo p  a n d d ep lo ye d  A uto m ate  M eetin g c o n ve rs a tio n  f r o m\nsp eech  t o  t e x t u sin g S p eake r D ia riz a tio n  a n d t r a n sc rib e.\nD esig n ,  D eve lo p  a n d D ep lo ye d  C on ve rs a tio n  S u m mariz a tio n  u sin g	  	\nL Lam a-3  ( L LM ) a n d d ep lo ye d  o n  A W S.\n O bje ct D ete ctio n :  D ete ctio n  o f P PE e q u ip m en t f r o m  v id eo  a n d\npro vid in g d ata  w it h  a ll t h e A naly sis  u sin g Y olo .\nD esig n ,  d eve lo p  a n d I m ple m en t F ace  R eco g n it io n  u sin g M ob il e  F ace\nN et f o r e d ge d evic e s l ik e  m ob il e  P hon e e tc .\nD ata  s c ie n ce  m od el t o  p re d ic t c h an ce s o f w in nin g a  p arty  u sin g D ata\nA naly sis  a n d M ach in e l e arn in g.\nfin etu n ed  L arg e L an gu ag e M od els  o n  A W S c lo u d o n  c u sto m  D ata se t\nu sin g Q uan tiz a tio n  a n d f in etu n in g A lg orit h m s.\nL ead in g a  T eam  o f 1 0 p eo p le s i n clu din g M L e n gin eers  a n d F u ll s ta ck\nd eve lo p ers	\nG re enT ech  I T S  L LP,  D elh i	\nD esig n ,  d eve lo p  a n d d ep lo ye d  T ra ffic  f lo w  A naly sis :  B uil d  M ult ip le\nt r a ffic  a n aly sis  t o ols  f o r m ult ip le  s c e n ario  i n  t o ll m an ag em en t f r o m\nsc ra tc h .\nV id eo  I n cid en t D ete ctio n  S yste m :  D ete ct A ccid en ts ,  F ir e ,  s m oke ,\nW ro n g s id e v e h ic le s,  s to p  v e h ic le s e tc .\nW ork in g o n  C om pan y P ro d u ct r e la te d  t o  C om pute r v is io n  a n d\ndesig n in g a lg orit h m  a n d t r a in in g D eep  L earn in g m od els  a n d L LM s.\nC on du cte d  r e se arc h  a n d p re p are d  r e p orts  o n  v a rio u s t o p ic s r e la te d  t o\nth e c o m pan y&#039;s  o p era tio n s a n d i n du str y  t r e n ds i n  m ach in e l e arn in g\nD om ain  l ik e  O CR ,  P atte rn  R eco g n it io n .	\nI sh ir  T ech no lo g ie s,  N oid a	\nPart o f a  T eam  o f D ata  S cie n tis ts ,  w ork e d  o n  v a rio u s M L a n d\nC om pute r V is io n  A pplic a tio n s a n d W ork e d  o n  M ach in e L  e arn in g L if e\nC ycle  l ik e  D ata  G ath erin g,  D ata  p re p ara tio n ,  D ata  P re  p ro ce ssin g,\nExp lo ra to ry  D ata  A naly sis ,  M od e t r a in in g.\nM an ag e S erv e r D ata b ase s u sin g S Q L S erv e r M an ag em en t S yste m  a n d\nap ply  v a rio u s T ech niq u es l ik e  D ata  R ep lic a tio n ,  D ata  B ack u p ,  D ata\nC lu ste rin g.	\nB .  T ech  i n  C om pute r S cie n ce	                       	2 0 15 -2 0 19	\nC G PA	 :  7 .7 6 / 10	\nP yth on\nM ach in e L earn in g	 	\nD eep  L earn in g\nC om pu te r V is io n	 	\nP re d ic tiv e A naly tic s	 	\nN LP\nD ata  S cie n ce	 	\nA W S C lo u d	 	\nLarg e L an gu ag e m od el,  G en era tiv e A I\nSQ L ,N O S Q L,  P in e C on e,  c h ro m a\nLan g C hain ,  l la m a i n dex\nP yto rc h ,  T en so rflo w	  	\nJ a v aS crip t,  H TM L	 	\nPro m pt E n gin eerin g\nM ach in e l e arn in g O pera tio n s ( M LO ps)\nD ata  S cie n ce  W it h  P yth on  o n  S im pli le arn .\nB ig  D ata  H ad oop  a n d S p ark  d eve lo p er\ntr a in in g o n  S im pile arn .\n M aste rin g D ata  S tr u ctu re  a n d A lg o rit h m s\nusin g C  a n d C ++.	\nM ach in e L earn in g E n gin eerC om pu te r V is io n  E n gin eerD ata B ase  A dm in is tr a to r I n te rnT eerth an ker M ah av eer U niv ers it y	g it h ub.c o m /D ark aid o	M AY  2 0 22 -  P R ESE N TAU G  2 0 21 -  M AY  2 0 22	 	F E B  2 0 20  -  F E B  2 0 21',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 14:33:28',0),(421,102,76,NULL,NULL,'76_102_1769407210669d1c38ede96_AM-Sai-Subhasish-Rout.pdf','AM-Sai-Subhasish-Rout.pdf','Sai S u bh asis h  R ou t	\nAI/M L E ngin eer	\nsa is u bh asis h ro u t7 77@ gm ail. c o m	 	+ 91 9 556 332 113	 	B an galo re , I n dia	 	\nw ww.li n ked in .c o m /in /s u bh asis h -s a i	 	h ttp s:/ /g it h u b.c o m /s a is u bh asis h	 	\nh ttp s:/ /m ed iu m .c o m /@ sa is u bh asis h ro u t7 77	 	\nS U M MARY\nExp erie n ce d  A I D eve lo p er 	h ig h ly  p ro fic ie n t i n  d eve lo p in g	 f u ll- s ta ck  p ro je cts  w it h  e ffic ie n t p ip eli n es. S tr o n g	\nback g ro u n d  in   m ath em atic s,  pro g ra m min g,  an d  data   m an ip u la tio n ,  tr a n sla tin g 	b u sin ess  go als	  in to	\nim pactfu l,  d ata -d riv e n  	so lu tio n s	. S pecia li z es i n  p re d ic tiv e  m od eli n g, s tr a te g ic  i m ple m en ta tio n  o f d ata -d riv e n	\na p pro ach es,  an d op tim iz in g  d ecis io n -m ak in g  p ro ce sse s.  Skille d  in  P ro m pt  E ngin eerin g  an d effe ctiv e ly\nc o m mun ic a te s  te ch n ic a l  co n ce p ts   to  n on -te ch n ic a l  sta k eh old ers.  C om mit te d  to  	co n tin uou s  le a rn in g	 a n d	\nsta yin g a b re a st o f i n du str y  t r e n ds t o  	in nova te  	w it h in  t h e d yn am ic  f ie ld  o f d ata  s c ie n ce	\nS K IL L S\nData b ase s\nM yS Q L, M on goD B\nDeep  L ea rn in g\nN LP, T ra n sfo rm ers, G en era tiv e  A I, P yT o rc h ,	 	\nTen so rflo w , P ro m pt E ngin eerin g\nD ata  A naly tic s\nP ow er B I\nM ach in e L ea rn in g\nScik it - le a rn\nV ersio n  C on tr o l\nG it h u b\nG it h u b a ctio n s C I- C D  A IO ps	\nP ro gra m min g L an gu ag e: P yth on\nFastA PI, P an das, N um py, S ele n iu m , L an gch ain ,	 	\nR eq u ests , B eu tif u ls o u p4, F la sk\nD ata  P ro ce ssin g\nPyS park , K afk a, A ir flo w , A pach e,\nC lo u d\nAW S S 3, A W S E C 2, A W S S Q S, A W S C lo u dw atc h\nD ata  M on it o rin g a n d G ove rn an ce\nD ata  d rif t\nC on ta in eriz a tio n  a n d O rc h estr a tio n\nD ock er	\nP R O FE SSIO N AL E X PE R IE N CE\nAI 	E ngin eer 	- S D E 	1	\nEnga gesp h ere  	T ech n olo g y 	P vt 	L td	\n&bull;D eve lo p  a n d m ain ta in  	A I s o lu tio n s	 t o  e n han ce  b u sin ess p ro ce sse s, 	a u to m ate	 	\nta sk s	, a n d d riv e  g ro w th .	\n05/2 0 24 &ndash;  p re se n t\nB en galu ru ,	 I n dia	\n&bull;D esig n  	m od el a rc h it e ctu re s	 f o r v a rio u s a p pli c a tio n s a n d s e le ct t h e a p pro p ria te	 	\nte ch n olo g y s ta ck s.\n&bull;D eve lo p  a n d 	op tim iz e  p ro m pts	 t o  e x tr a ct t h e r ig h t i n fo rm atio n  f r o m  l a n gu ag e	 	\nm od els .\n&bull;D esig n  a n d i m ple m en t 	A PIs  	fo r 	e ffic ie n t d ata  r e tr ie va l	, p ro ce ssin g, a n d s to ra g e.	\n&bull;C olla b ora te  w it h  f r o n t- e n d d eve lo p ers  t o  	in te g ra te  b ack -e n d	 s e rv ic e s w it h  f r o n t-	\ne n d c o m pon en ts .	\nA sso cia te  	D ata  	S cie n tis t	\niN eu ro n  	In te lli g en ce  	P vt.  	L td .	\n&bull;Im ple m en te d  A I- d riv e n  I n nova tio n s f o r E du ca tio n al P la tfo rm  E nhan ce m en t.	\n1 0 /2 0 23 &ndash;  0 3/2 0 24\nB an galo re ,	 I n dia	\n&bull;A ch ie ve d  a  s ig n if ic a n t 	4 5%  i m pro ve m en t	 i n  a d m in is tr a tiv e  e ffic ie n cy  t h ro u gh  q u iz	 	\na n d a ssig n m en t 	a u to m atio n  	in  t h e L ea rn in g M an ag em en t S yste m  ( L M S).	\n&bull;S tr e a m li n ed  t h e o ve ra ll u se r e x p erie n ce  w it h in  t h e L M S,r e su lt in g i n  a  	6 0 %	 	\nd ecre a se  i n  o ve ra ll c li c k s	.	\n&bull;E nhan ce d  c u sto m er s a tis fa ctio n  b y r e d u cin g r e sp on se  t im e t o  8 0 %  	th ro u gh  t h e	 	\nd eve lo p m en t o f a n  a u to m ate d  e m ail r e p ly  s y ste m .\n&bull;Engin eere d  a n  e ffic ie n t A I c h at s y ste m , 	o p tim iz in g t e a m  s iz e  b y 3 0 %	, a n d	 	\nre d u cin g c u sto m er q u ery  r e so lu tio n  t im e.\n&bull;L ed  t o  i m pro ve d  e ffic ie n cy  a n d r e so u rc e  u tili z a tio n	, s tr e a m li n in g o p era tio n s	 	\nacro ss d if fe re n t a sp ects  o f t h e e d u ca tio n al p la tfo rm .\n\nData  	S cie n ce  	T ech  	C on su lt a n t	\niN eu ro n  	In te lli g en ce  	P vt.  	L td .	\n&bull;D eve lo p ed  	w eb  c ra w le rs	, e x tr a ctin g d ata  f r o m  d iv e rs e  w eb sit e s.	\n0 8/2 0 23 &ndash;  1 0 /2 0 23\nB an galo re ,	 I n dia	\n&bull;E sta b li s h ed  a  	p ip eli n e f o r d ata  c le a n in g	 a n d s to ra g e i n  a  r e la tio n al d ata b ase .	\n&bull;S ch ed u le d  t h e r e fr e sh in g p ro ce ss e ve ry  t h re e d ays u sin g a  	cro n  j o b	 t o  m ain ta in	 	\nu pto -d ate  i n fo rm atio n .	\nA sso cia te  	Q A 	Engin eer	\nIn ov a are  	C lo u ds 	S oft w are  	S olu tio n s 	P vt.  	L td .	\n&bull;D eve lo p ed  a n d m ain ta in ed  	au to m atio n  t e st s c rip ts	 u sin g P yth on  f o r r e g re ssio n	 	\nte stin g.	\n12 /2 0 21 &ndash;  0 8/2 0 23	\nB hu ban esw ar,	 I n dia	\n&bull;P artic ip ate d  i n  d aily  	sc ru m  m eetin gs	, p ro vid in g s ta tu s u pd ate s o n  t e stin g	 	\npro g re ss.\n&bull;P erfo rm ed  r e g re ssio n  t e stin g a n d f u n ctio n al t e stin g o n  n ew  f e a tu re s a n d	 	\nen han ce m en ts .	\nG ra d u ate  	E ngin eer 	T ra in ee	\nS hah i 	E xp orts  	P vt.  	L td .	\n&bull;W ork ed  a s t r a in ee i n  I n du str ia l E ngin eerin g D ep artm en t.	\n1 0 /2 0 19  &ndash;  0 5/2 0 20	\nB hu ban esw ar,	 I n dia	\nP R O JE C TS\nAI-D riv e n  	C usto m er 	S u pport 	C hat 	S yste m	\nIn nov a tiv e 	C hat 	S upport 	S yste m :	\n&bull;Acco m pli s h ed  a  s ig n if ic a n t 	4 0 %  r e d u ctio n	 i n  r e sp on se  t im es, c o n tr ib u tin g t o	 	\no ve ra ll e ffic ie n cy  i n  r e so lv in g s tu den ts &#039;  q u erie s.	\n11/2 0 23 &ndash;  0 2/2 0 24	\n&bull;A ppli e d  d ata -d riv e n  i n sig h ts  t o  p ers o n ali z e i n te ra ctio n s b ase d  o n  	in div id u al	 	\np re fe re n ce s	.	\nA uto m ate d -E du ca tio n al 	A sse ssm en t 	S yste m	\n&bull;L eve ra g ed  L L M  m od el f o r 	a u to  	assig n m en t, q u iz  g en era tio n , a n d s tr e a m li n in g	 	\ngra d in g p ro ce sse s.	\n10 /2 0 23 &ndash;  1 1/2 0 23	\nP E R SO N AL P R O JE C TS\nThyro id  	D is e a se  	D ete ctio n	\n&bull;A ddre ssin g t h e p re va le n ce  o f t h yro id  d is e a se  a s a  c o m mon  e n docrin e d is o rd er, I	 	\nd eve lo p ed  a n  M L m od el t o  a ssis t h osp it a ls  i n  p re d ic tin g t h yro id  c o n dit io n s b ase d	 	\no n  p atie n t d ata .	\n0 1/2 0 23 &ndash;  0 3/2 0 23	\n&bull;T ech  S ta ck : P yth on , M L a lg orit h m s, D ock er, A W S, A ir flo w , M on goD B, F la sk , F ast	 	\nA PI, A W S, G it h u b-a ctio n s, M LO ps\n&bull;A tta in ed  a cc u ra cy  o f 9 9.8 5%	 u sin g X gB oost.	\nE D UCATIO N\nBach elo rs 	o f 	T ech n olo gy	\nIT E R , 	S O A 	U niv ersit y	\nM ech an ic a l E ngin eerin g	\n08/2 0 15  &ndash;  0 5/2 0 19	\nB hu ban esw ar,	 I n dia	\nC ER TIF IC ATE S	\nFu ll S ta ck  D ata  S cie n ce\nW hat i s  G en era tiv e  A I ?	\nT he D ata  S cie n ce  o f	 	\nH ea lt h ca re , M ed ic in e, a n d	 	\nP ubli c  H ea lt h\nP yth on  f o r D ata  S cie n ce	\nG en era tiv e  A I: W ork in g w it h	 	\nL arg e L an gu ag e M od els\nS ta tis tic s 1 0 1	\nA W AR D S\nBest 	v o lu n te er 	o f 	th e 	ye a r	\nA w ard ed  b y t h e N GO  &quot; T he H elp in g E ye&quot; i n  2 0 20 .',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 14:33:28',0),(422,102,76,NULL,NULL,'76_102_1687535226669d1c38ee03c_Arifiya_Khan-Resume_(1).pdf','Arifiya Khan-Resume (1).pdf','Arifiya Khan\nEmail: arifiyakhan@gmail.com Linkedin : linkedin.com/in/arifiya-khan\nMobile: +91-8172993811 Location: Bangalore, Karnataka\nSkills Summary &bull;\nFrameworks : TensorFlow, PyTorch, Keras, Large Language Model (LLM)\n&bull; Libraries : Transformers, NumPy, Pandas, Scikit-learn, spaCy, NLTK\n&bull; NLP Tools : Topic Modeling, Named Entity Recognition (NER),Sentiment Analysis, Text Summarization, Natural\nLanguage Processing (NLP), Prompt Engineering, Retrieval-Augmented Generation (RAG)\n&bull; Platforms : AWS(Amazon Web Services), macOS, Windows\n&bull; Languages : Python, C++, SQL\n&bull; Soft Skills : Leadership, Writing, Public Speaking, Time Management\nExperience &bull;\nTata Consultancy Services\nBangalore,karnataka\nMachine Learning Developer (Ful l-time) Oct 2022 - Present\n◦Performed large-scale text analytics on 2 Mn+ agent-customer chat data for Apple(Client) at TCS, leveraging\nadvanced NLP techniques &ndash; Topic Modeling, Text Classification,Text Summarisation(BART),spaCy,\nNER, LLMs, and sentiment analysis .\n◦ Enabled data-driven service enhancement by identifying pain points &ndash; problematic product names through NER,\ngeographic issue hotspots through spatial mapping, and areas for improvement through insights from exhaustive\ndata cleaning, preprocessing, and text summarization.\n&bull; Eloquent Advisory Pvt. Ltd.\nRemote\nTest Automation Architect (Ful l-time) Sept 2021 - Sept 2022\n◦Spearheaded end-to-end manual and automated testing processes for Thinkly App, utilizing Jira,\nBrowserStack, and AccelQ. Transitioned from intern to full-time position, solely managing testing, coordinating\nwith developers, and driving 180% quality improvements . Recognized for adaptability, dedication, and\nexceptional testing oversight, consistently delivering high-quality results and earning performance- based\nincrements and commendations.\nProjects &bull;\nCab Fare Prediction - Numpy, Pandas, Scikit-learn, Linear Regression, Random Forest : Developed a Cab Fare\nPrediction system employing linear regression and random forest algorithms, achieving 90% accuracy in predicting fare\namounts for city cab rides.\n&bull; Face recognition model - Tensorflow, Keras, MobileNet, Convolutional Neural Networks (CNN), OpenCV :\nEngineered a Face Recognition model using MobileNet and CNN with TensorFlow and Keras, enhancing effectiveness and\nachieving an accuracy of 85% in identifying specific individuals, including family members.\nCertifications &amp; Trainings &bull;\nData Analyst, edWisor (March 2020-Aug 2020) : Mastered scientific algorithms to extract insights from unstructured\ndata using R and Python, enhancing analytical skills for data-driven decision-making.\n&bull; Hybrid Multi Cloud, LinuxWorld Informatics Pvt Ltd : Proficient in AWS, EKS and Terraform for hybrid multi-cloud\nenvironments and cloud infrastructure automation.\n&bull; Google Cloud Certified Associate Cloud Engineer : Certified Associate Cloud Engineer by GCP.Utilized Google Cloud\nservices such as Compute Engine, Kubernetes Engine, Cloud Storage, BigQuery and Cloud SQL\nEducation &bull;\nI.E.T.,Dr. Ram Manohar Lohia Avadh University\nFaizabad, India\nBachelor of Technology - Computer Science and Engineering(CSE); GPA: 82%, Rank:1July 2018 - June 2022\nCourses: Operating Systems, Data Structures, Analysis Of Algorithms, Machine Learning, Networking, Databases\n&bull; Nirmala Convent Sr. Sec. School\nRenukoot, India\nIntermediate &mdash; Science (PCM) July 2015 - June 2017\nCourses: Physics, Chemistry, Mathematics, Computer Science\nHonors and Awards &bull;\nBest Team Award - Awarded by Tata Consultancy Services Limited (TCS),2023\n&bull; Outstanding performer Prize,Web Development - Awarded by softpro India Computer Technologies (P) Ltd,2018',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 14:33:28',0),(423,102,76,NULL,NULL,'76_102_1319945693669d1c38ee20b_chandra-GenairesumeV3.pdf','chandra-GenairesumeV3.pdf','CHANDRA MOULI	 	\nData Scientist / AI &amp; ML Engineer	 / GenAI 	Developer	 	\n \n \nDynamic and results	-driven Data Scientist with 	4+ years	 of extensive experience leveraging expertise in	 Generative AI,	 	\nArtificial	 Intelligence and Machine Learning. Targeting 	opportunities in Bangalore, Hyderabad, Remote, and Gulf Countries.	 	 	\n+91	-8309047501	 	  	\ntchandramouli8978@gmail.com	 	  	\nlinkedin.com/in/	chandra	-mouli07	/ 	\n 	\nCORE COMPETENCIES	 	  	\nGenerative AI &amp; LLMs	 	\n \n                           	Prompt Engineering	 	\n 	\n                     	AI/ML	 	\n 	\nDeep Learning	 	\n 	\nNatural Language Processing	 	\n 	\n  Computer Vision	 	\n 	\nPredictive Analytics      	 	\n 	\nMathematical/Statistical Concepts	 	\n 	\n          	 MLOps / LLMOps	 	\n 	\nData Analysis &amp; 	Visualization	 	\n \n \n \n 	\nSOFT SKILLS	 	  	\nAnalytical	 	  	\nInnovative &amp; Critical	 	 	Thinking	 	 	\nCommunication Time \nManagement Leadership \n&amp; Team Collab. \nPresentation	 	\n  	\nPERSONAL DETAILS	 	  \n&bull; 	Date of Birth:	 23	rd May	 1998	 	 &bull; 	Address:	 NR layout, Garvebhavipalya, 	\nBangalore 	-560068	 	 &bull; 	Languages Known:	 English	, Telugu, 	\nTamil &amp; Hindi	 	 	\n 	\nPROFILE SUMMARY	 	  &bull; 	Possess experience in 	Artificial Intelligence	 and 	Machine Learning	 across 	\nFinancial Services	 and BFSI industry	. 	 &bull; 	Spearheading  a  team at 	CAPCO Technologies	,  focusing  on developing cutting	-	\nedge  AI  solutions  such  as 	LLMs,  Generative  AI	 Chatbot  and  advanced  text 	\nclassification models, driving innovation and operational efficiency.	 	\n 	\n&bull; 	Proficient in	 Generative 	AI, LLMs,	 Prompt engineering	 and more.	 	 &bull; 	Proficient in 	Python	, API, Data Manipulation, Automation, 	SQL, NoSQL	, 	\nData Analysis, Data Wrangling	, and more.	 	 &bull; 	Acknowledged  for 	creating  innovative  AI  solutions	 that  revolutionized 	\nbanking  automation  and  provided  tailored  recommendations,  enhancing \noperational efficien	cy and customer satisfaction.	 	 &bull; 	Proficient  in  conducting 	Statistical  Analysis,	 developing 	Predictive  Models	, 	\nimplementing 	Time  Series  Forecasting	 techniques,  and  conducting 	A/B 	\nTesting	 to enhance decision	-making processes and drive business growth.	 	 \n&bull; 	Proven 	track record in 	strategic thinking, team leadership	, and 	data	-	\ndriven decision	-making	. 	 	\nEDUCATION	 	  	\nB.Tech. in 	Electronic and Communication Engineering	 from 	REVA 	\nUNIVERSITY	 | 20	19	 | 6.9	 GPA	 	 	HSC	 from Sri Chaitanya educational institutions | 2015 | 92.9%	 	\n 	\n CERTIFICATIONS	 	  \n&bull; 	Generative AI Fundamentals |Databricks | 2024	 	 &bull; 	Data Analysis and visualization Bootcamp | Udemy | 20	22	 	 &bull; 	Data science Foundation |IABAC| 2022	 	 &bull; 	Data Science Bootcamp | Data mites | 2022	 	\n 	\n \nIT SKILLS	 	  \n&bull; 	Development Tools / IDEs:	 PyCharm, Jupyter Notebook, Visual Studio Code	 	 &bull; 	Web Scraping:	 Beautiful soup	, Scrapy	 	 &bull; 	Web Framework:	 FastAPI	 	 &bull; 	Databases:	 MySQL, Microsoft	 SQL Server, Neo4j	 	 &bull; 	Data Analysis &amp; Visualization	: Pandas, Numpy, sciPy, Matplotlib, Seaborn, 	\nPlotly, PowerBI	 	 &bull; 	Generative  AI	: LangChain,  OpenAI  GPT  3,  GPT  4,  Llama2,  Mistral  LLM,  BERT, 	\nHugging  Face Transformers and  Embedding  Models, Tensorflow  Hub,  Facebook  AI \nSemantic Search, Pinecone VectorDB, Chroma DB, RAG	 &amp; Langchain agents	 	\n 	\n&bull; 	Prompt  Engineering	: Zero	-shot  prompting,  One	-shot  prompting,  Few  shot 	\nlearning, 	Step back prompting &amp; Prompt chaining.	 	 &bull; 	AI / ML:	 TensorFlow, Keras, PyTorch, Scikit	-learn, XGBoost, AWS 	\nSageMaker, Azure ML Studio	 	 &bull; 	Deep Learning:	 CUDA, Caffe	 	 &bull; 	NLP:	 BERT, GPT, NLTK, SpaCy	 	 &bull; 	Computer Vision:	 YOLO, OpenCV, Tesseract 	OCR,	 Scikit	-Image, GPT4 	\nVision	 	  &bull; 	Version Control &amp; DevOps / MLOps / LLMOps:	 Git, Bitbucket, Jira, 	\nDocker, Kubernetes, MLflow, Langsmith, CI/CD\n\nWORK EXPERIENCE	 	  \nData Science 	Consultant |	 CAPCO Technologies, Bengaluru | 	Jan	&rsquo;22-Present 	\nKey Result Areas:	 	 &bull; 	Leading a dynamic team consisting of a Data Analyst and a Junior Machine Learning Engineer, actively guiding project directio	n 	\nand ensuring seamless collaboration.	 	 &bull; 	Spearheading the development of cutting	-edge AI solutions, including a state	-of-the	-art Large Language Models, Generative AI 	\nChatbot, resulting in significant enhancements in client engagement and operational efficiency.	 	 &bull; 	Implementing AI	-driven models, 	particularly in text classification	, thereby automating intricate workflow and delivering 	\nsubstantial cost and time savings for clients.	 	\n  &bull; 	Pushing the boundaries in the application of AI within the banking sector, particularly in automating customer servic	e and 	\noffering personalized financial recommendations.	 	\n 	\n&bull; 	Implementing GenAI	-driven solutions specifically like Conversational 	Private kb&rsquo;s, Content 	Generation 	Automation, Content 	\ncomparison delivering automated customisa	ble	 solutions for clients.	 	\n 	\n&bull; 	Spearhead	ing the development of GenAI Conversation chatbots on Private Kb&rsquo;s on pr	ivate 	data	 sources	 leveraging RAG 	\n(Retrieval Augmented Generation).	 	\n 	\n&bull; 	Developed a Language summarization Agent by leveraging Lang chain map reduce chains and advance prompt engineering \nwhich can summarize complex docs like regulatory notices etc. &amp; extract insights, improving efficiency and accuracy of \nanalysis.	 	\n 	\n&bull; 	Implementing 	various range 	of 	advanced 	prompt engineering techniques like 	Zero	-shot, 	One	-shot 	 &amp; Few Shot 	learning to 	\nAutomate	 tailored C	ontent generation by reducing hallucination of the LLMs	 delivering optimised cost and time savings for 	\nclients.	 	\n 	\n&bull; 	Implemented advanced LLMs, including local and API	-based models, to solve complex problems and provide tailored solutions, 	\nsignificantly boosting client engagement.	 	\n 	\n&bull; 	Facilitating knowledge transfer and capacity building within the team by conducting training sessions on the latest AI \ntechnologies and methodologies.	 	\n 	\n 	\n \nData Science 	Trainee | Change	 Pond technologies	  , Chennai 	|Jan &lsquo;	20	-Jan&rsquo;2	2 	\nKey Result Areas:	 	\n  &bull; 	Contributed in development of machine learning model 	to enhance the quality and efficiency of business processes involving 	\nimage processing, OCR, text mining, and information extraction	. 	 &bull; 	Cleaned and pre	-processed the unstructured image data 	to detect tables and extract relevant information using advanced 	\nimage processing techniques.	 	\n 	\n&bull; 	Leveraged PyTesseract for 	OCR 	to accurately extract data from images, facilitating seamless integration with	 business 	\nworkflows.	 	\n 	\n&bull; 	Involved in streamlining the business processes by automating repetitive tasks 	through the application of machine learning 	\nand RPA, resulting in significant time and cost savings.\n\nANNEXURE	 	\n  \nProjects Undertaken 	at CAPCO Technologies	 	 Project 1:	 AI-Powered Custom Knowledge Base Chatbot for Enhanced Data Retrieval	 	 Initiated  the  development  of  a  cutting	- edge  AI	-powered  Chatbot  incorporating  Retrieval  Augmented  Generation,  aimed  at  transforming 	\ninformation retriev	al across varied data formats such as scanned PDFs, text documents, and databases. This endeavor entailed leveraging 	\nLangChain and Facebook AI Semantic Search for comprehensive comprehension and efficient querying, alongside advanced Large La	nguage 	\nModels 	(LLMs)  like  GPT	-3.5/4,  Mistral  LLM,  and  LLAMA2  for  contextually  relevant  responses.  Proficiently  integrated  Hugging  Face 	\nembedding  models,  employing  BERT  to  augment  language  modeling  capabilities.  The  application  of  Generative  AI  and  advanced  NLP	 	\ntechnique	s  facilitated  natural  language  queries	.Python  acted  as  the  primary  programming  language,  supported  by  FAISS  for  streamlined 	\nsimilarity search and Transformers for data manipulation, ensuring a seamless and precise knowledge retrieval experience.	 	  \nProject	 2: AI-Powered Language Summarization Agent	 	 Developed an advanced AI	-powered Language Summarization Agent, utilizing LangChain&#039;s map	-reduce techniques for summarizing 	\nsmall documents and KMeans clustering for large document summaries. This project focused	 on transforming the summarization of 	\ncomplex documents, such as regulatory notices, to extract critical insights and improve analysis efficiency. Applied advanced	 prompt 	\nengineering  techniques  to  ensure  high	-quality,  contextually  relevant  summaries.  Lever	aged  the  capabilities  of  advanced  large 	\nlanguage  models  (LLMs),  significantly  enhancing  the  accuracy  and  speed  of  data  processing.  Utilized  Python  as  the  primary \nprogramming language, ensuring seamless implementation	  and integration of the summarization a	gent into existing workflows.	 	  \nProject 3:	 AI-Powered 	Automated SAR Narrative Generation	 	\nDevelop	ed 	an  innovative  AI	-powered  solution  for  automating  Suspicious  Activity  Report  (SAR)  narrative  generation.  Leveraging 	\nadvanced  prompt  engineering  techniques, 	the  application  streamlines  the  otherwise  manual  and  tedious  process  of  writing  SAR 	\nreports based on input case details provided.	 	\nUtilizing 	Lang  Chain	 and  state	-of-the	-art  Large  Language  Models  (LLMs),  the  system  efficiently  generates  SAR  narratives  with 	\nunparalleled  accuracy  and  efficiency.  Advanced  prompting  techniques  were  instrumental  in  guiding  the  AI  model  to  produce \ncontextually relevant and lega	lly compliant reports.	 	\nBy automating SAR report writing, the application significantly reduces time and costs for clients, eliminating the need for 	manual 	-	\nintensive processes. This transformative solution not only enhances operational efficiency but also 	ensures consistency and accuracy 	\nin compliance documentation.	 	  \nProject 4:	 Automated Data Collection and Intelligent Text Classification for Enhanced Decision Support	 	 Championed the initiation of an advanced Automated Data Collection and Intelligent Clas	sification for Enhanced Decision Support 	\nsystem. Employed state	-of-the	-art Natural Language Processing (NLP) techniques and Machine Learning algorithms to automate the 	\nanalysis  and  categorization  of  regulatory  news  articles  from  over  1600  sources.  Achieved	 an  impressive  accuracy  rate  of  83% 	\nthrough  the  training  of a BERT model  on  extensive  datasets.  Engineered  a  FastAPI  application  to  seamlessly  incorporate  Machin	e 	\nLearning models, enabling smooth interaction with data acquired by RPA bots and APIs. Monthly	 model retraining initiatives were 	\nimplemented to uphold accuracy levels and ensure alignment with current trends and developments.	 	\n  Project 5	: A	I-Powered Credit 	Memo 	Narrative Generation	 	 Initiated the development of an AI	-powered solution tailored for 	the automated generation of Credit Memos, a pivotal component in 	\nthe credit assessment process. By harnessing LangChain, advanced Large Language Models (LLMs), and innovative prompt chaining	 	\ntechniques, the application revolutionizes the analysis of entity	 credit requests based on 10K reports and other pertinent data.	 	\nTraditionally a 	manual	-intensive task, the generation of Credit Memos typically involves multiple teams within financial institutions. 	\nHowever, 	this 	solution streamlines this process by automa	ting data analysis and narrative generation, providing loan officers with 	\ncomprehensive insights to facilitate informed decision	-making.	 	\nThe utilization of prompt chaining techniques orchestrates the integration of task	-specific prompts, enabling the model	 to synthesize 	\ninformation  from  diverse  sources  into  cohesive  narrative  formats.  By  amalgamating  insights gleaned  from  entity  10K  reports  an	d 	\ncredit  requests,  the  generated  Credit  Memos  offer  a  holistic  view  of  creditworthiness,  empowering  loan  officers  to	 make  well	-	\ninformed decisions efficiently.	 	\nThrough  the  amalgamation  of  LLMS,  LangChain,  and  prompt  chaining  techniques,  this  innovative  solution  optimizes  the  credit \nassessment process, enhancing operational efficiency and decision	-making accuracy within f	inancial institutions.	 	  \nProject 6:	 AI 	-Powered Content Comparison Agent	 	\nDeveloped an AI	-powered agent for seamless comparison of content across images, text, and hybrid formats. Integrated LangChain, 	\nGPT	-4  Vision,  and  GPT	-3.5  with  advanced  prompt 	techniques  for  precise  analysis.  Enhances  efficiency  and  accuracy  in  content 	\ncomparisons for informed decision	-making.',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 14:33:28',0),(424,102,76,NULL,NULL,'76_102_1456298731669d1c38ee54c_Chinmay_Deshmukh_resume.pdf','Chinmay_Deshmukh_resume.pdf','Domain Specific Chatbot	\nLe ve ra ge d  t h e R asa  f r a m ew ork  t o  a ch ie ve  a  9 9%  a ccu ra cy  r a te  i n  c la ssif y in g 2 6  d is tin ct i n te n ts .\nD esig n ed  t h e c h atb ot t o  e n ab le  d ir e ct i n te ra ctio n  b etw een  u se rs  a n d  t h e d ata b ase , f a cil it a tin g s e am le ss\nre tr ie val o r u p d atin g o f v alid ate d  u se r d ata .	\nDeepstream Auto Annotation Tool	\nR ed uce d  a b out 7 0 %  o f t im e i n  M ach in e L e arn in g c y cle  b y a u to m atic a lly  a n nota tin g i m age s.\nC ours e ra  S p ecia liz a tio n s ( D ata  S cie n ce / M L /  D L /  A I)	 	\nD ata b ric ks C ertif ie d  A sso cia te  D eve lo p er f o r A pach e S p ark  3 .0\nG oogle  C lo ud  Q w ik la b s\nD ata ca m p C ertif ic a tio n s ( D ata\nS cie n ce  /  D evo p s /  L LM s)	C hin m ay  D esh m ukh\nData  S cie n tis t	  |  D ata  E n gin eer |  M en to r |  D eve lo p er	\nW ork  E xp erie n ce\nData Scientist, 	LTIMindtree	                                                                                                  	Aug 2022 - Present	\nDep lo ye d  a  L arg e  L an gu age  M od el ( L LM ) \nc u ttin g G PU  c o sts . W ork e d  o n  M is tr a l, Q w en , L LaM A, e tc .\nD esig n ed  a  p ip elin e t o  d ete ct a re as o f i n te re st i n  i m age s a n d  e xtr a ct t e xt f o r a n aly sis .\nM ech an iz e d  a n  u n m an ned  s c rip t t h at c re ate s a  c o m pute  i n  A zu re  w hen  u se r r e q uests .\nIm pro ve d  p re -e xis tin g c o d e, r e su lt in g i n  7 5 %  r e d uctio n  o f e xe cu tio n  t im e.\nLe ve ra ge d  A zu re  M ach in e L e arn in g ( A zu re  M L) f o r b uil d in g, t r a in in g, a n d  d ep lo yin g m ach in e l e arn in g m od els .\nE m plo ye d  A zu re  C osm osD B w it h  P ostg re S Q L t o  s to re  e m bed din gs a s a  v e cto r d ata b ase , o p tim iz in g d ata\nr e tr ie val a n d  s to ra ge  f o r m ach in e l e arn in g a p plic a tio n s.\nW ork e d  w it h  L an gC hain  t o  c re ate  s o p his tic a te d  p ro m pts  a n d  a ge n tic  f lo w s, e n ab lin g p re cis e  a n d  e ffic ie n t\noutp uts  f r o m  g iv e n  i n p uts .\nM an age d  c u sto m  e n vir o n m en ts  w it h  D ocke r f o r c o n sis te n t a n d  e ffic ie n t d ep lo ym en t.\nD eve lo p ed  b acke n d  A PIs  a n d  w ork flo w s u sin g D ja n go  t o  i n te ra ct w it h  A zu re  M L a n d  c re ate  e n d poin ts .	\nMentor,	  Coursera	                                                                                                                  	Aug 2021 - May 2022	\nM en to r f o r d eep le arn in g.a i c o urs e s a v ail a b le  o n  C ours e ra .	\n+9 1 8 36 956 686 7bhagy e sh .d esh m ukh 29 @ gm ail .c o mIn d ir a n aga r, B an ga lo re ,	 I n d ia , 5 6 0 008.	Data Scientist, Ease my AI	                                                                                                  	Sep 2021 - Dec 2021	\nA uto m ate d  r e cu rrin g t a sk s, r e d uce d  2 0 %  t im e r e q uir e d  f o r d eve lo p in g c o m pute r v is io n  s o lu tio n s.\nD eve lo p ed  a  c o d e t o  e n ab le  r a p id  l iv e  s tr e am in g f r o m  m ult ip le  c a m era s w hil e  m in im iz in g t h e u til iz a tio n  o f\nre so urc e s.	 	\nB uil t  a  p yth on  p acka ge  t h at a u to m ate s c o m pute r v is io n  t a sk s.	\nE d uca tio n\nBachelor&rsquo;s in Computer Engineering, University of Mumbai	                               	Jun 2018 - Aug 2022	\nCG PA : 	9 .1 5 /1 0 .0 0	\nhttp s:/ /c h in m ay -d .n etlif y .a p p\nP ro je cts\nITSM Webapp	\nW ork e d  o n  b acke n d , f r o n te n d  a n d  c o n ta in eriz a tio n  p art o f I T S M  w eb ap p b ase d  o n  I T IL  v 3 .	\nPythonC++JavascriptHTMLCSSMySQLPostgreSQLGCPAzureKubernetesDockerLinuxTensorflowNLPMachine LearningRasaDeeplearningPytorchDjangoReactMLOpsGen AIDSARAGTerraformApache Spark\nA cco m plis h m en ts	\nIn d ustr y  A dvis o r	 a t 	S -V YA SA  U niv e rs it y	 f o r U G , P G  a n d  f a cu lt y  o f D ata  S cie n ce .	\nR an ke d  i n  t h e 	to p  1 0  o ut o f 4 0 0 t e am s 	in  a  g lo b al h acka th on  ( L T IM in d tr e e S o lv ath on  2 0 23 ).	 	\nG uid ed  1 0 0&#039;s  o f s tu d en ts	 i n to  M L, D L. I n vit e d  b y D Y P atil  U niv e rs it y  a s a  w ork sh op  s p eake r.	\nC ertif ic a tio n s\nCil c k t h e i c o n s t o\nv ie w  m y p ro fil e s',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 14:33:28',0),(425,102,76,NULL,NULL,'76_102_1271150398669d1c38ee6f4_Hiten-Jain-Job-Resume.pdf','Hiten-Jain-Job-Resume.pdf','Hiten Jain\n+91-7424855035 &bull; hitenjain0509@gmail.com &bull; linkedin.com/in/hiten-jain-b44846210\nGenerative AI Engineer\nEnthusiastic AI graduate with a strong foundation in Natural Language Processing (NLP), Generative AI, and lar\ngelanguage models. Eager to leverage expertise from Deep Learning, NLP\n, and Machine Learning certifications to contribute tocutting-edge developments in the field. Passionate about the transformative potential of AI and NLP\n.SKILLS\nWORK EXPERIENCE\nNimbusnext &bull; Princeton, New Jersey\n, United States &middot; Remote &bull; 1\n1/2023 - Pr esent Junior Softwar\ne AssociateRenix Informatics &bull; Remote &bull; 09/2023 - 1\n1/2023 AI Resear\nch Intern EDUCA\nTION Bachelor of T\nechnology - BT ech in Electrical, Electr\nonics and Communications EngineeringSwami Keshvanand Institute of T\nechnology, Jaipur &bull; 01/2019\nPROJECTS\nChat W\nith multiple PDF&#039;s &bull; 07/2023 - 08/2023\nThis project built a conversational interface for searching through multiple PDFs. It utilized Langchain for the framework,\nHuggingface for text embeddings, and Faiss-CPU for ef\nficient text storage and retrieval. To generate context-aware responsesto user queries, it implemented lar\nge language models like google/flan-t5-xxl, GPT4All, and Lamini. Finally , a memoryfeature was incorporated to enable natural and coherent interactions across user queries.\nBoolean Question Answering System &bull; 03/2022 - 04/2022\nAI model for answering yes-or\n-no questions. The model is trained using an encoder -decoder architecture with LSTM layers.It learns from a collection of questions and their corresponding &quot;yes&quot; or &quot;no&quot; answers. The text is first prepared and turned\ninto numerical representations, then these are used to train the model to predict answers for future yes-no questions. Azure Openai\n&bull;\nDeep Learning\n&bull;\nDjango\n&bull; Langchain\n&bull;\nLar\nge Language Models (LLM) &bull;\nLlamaindex\n&bull; Machine Learning\n&bull;\nNatural Language Processing (NLP)\n&bull;\nPython\n&bull;\nImplemented retrieval-based techniques like RAG, resulting in a 25% increase in information retrieval ef\nficiency andultimately enhancing productivity for the team.&bull;\nDesigned and implemented generative AI applications leveraging cloud platforms like Azure and web frameworks like\nDjango.&bull;\nCustomized LLMs to meet project-specific requirements, enhancing the overall quality and functionality of software\nproducts.&bull;\nPrepared POCs, finetuned GPT\n, and used other various techniques like RAG.&bull;\nGained experience in all aspects of AI research, from data collection and preparation to model development and\nevaluation.&bull;\nCollaborated with a team of experienced researchers to solve complex AI problems.\n&bull;',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 14:33:28',0),(426,102,76,NULL,NULL,'76_102_781536741669d1c38ee83e_jasllm.pdf','jasllm.pdf','J	ASRAJ 	P	ATIL	 	\n7020453107	 | jasrajpatil2@gmail.com	 | www.linkedin.com/in/jasraj	-patil/	 | https://github.com/jasrajpatil	 	  \nEnergetic  and  innovative  Generative  AI  Engineer  eager  to  leverage  a  profound  background  in  AI, \nspecializing  in  vector  databases,  LLM  frameworks,  and  cloud  deployments.  Aims  to  apply  technical \nskills  and  creativity  in  developing  state	-of	-the	-art  AI  solutions  within  a  dynamic  team  setting.	 Also 	\nhaving 	experienced 	Data 	Analyst	 creating reports and visu	alizations with Power BI and handling data 	\nwith Python and SQL. 	 	\nSKILLS &amp; OTHER	 	\n&bull; 	Programming Languages	: Python, SQL	 	\n&bull; 	Frameworks &amp; Tools	: Pytorch, LangChain, LlamaIndex, Streamlit.	 	\n&bull; 	Generative AI Technologies	: Open	-source and paid LLM models (Llama2, 	Mistral, OpenAI, Gemma)	 	\n&bull; 	Vector Databases	: ChromaDB, Pinecone	 	\n&bull; 	Deployment Platforms	: AWS Bedrock, Hugging Face Spaces	 	\n&bull; 	AI/ML Techniques	: Fine	-tuning with custom data, vector embedding, NLP, neural network 	\noptimization, RAG.	 	\n&bull; 	Soft Skills	: Analytical thinking, p	roblem	-solving, teamwork, effective communication	 	\n \n PROFESSIONAL EXPERIENCE	 	\n \nInfocept	s, Nagpur	                                                                                                                             	                           	APR 	202	2 &ndash; May 2023	 	\nAssociate Data Analyst	 	\n&bull; 	Worked as Power BI Developer, on the Power BI environment Mig	ration project.	 	\n&bull; 	Created data models to support decision	-making processes.	 	\n&bull; 	Identified patterns and trends in large data sets and provided actionable insights.	 	\n&bull; 	Generated standard and custom reports to provide insights into business performance.	 	\n&bull; 	Designed and 	developed data pipelines to acquire, clean and process data.	 	\n&bull; 	Developed customized reports, summarizing and presenting data in visually appealing format.	 	\n&bull; 	Handled scripting tasks for extract, clean, and analyze data to derive actionable insights using Python	 	\n&bull; 	Provided technical support for troubleshooting analytics and reporting issues.	 	\n&bull; 	Participated in requirements meetings and data mapping sessions to understand business needs.	 	\n&bull; 	Developed and maintained courteous and effective working relationships.	 	\n 	\nESDS Soft	ware Solution	, Nashik	  	NOV 	202	1 &ndash;Apr 2022	 	\nTrainee	 	\n&bull; 	Built relationships with coworkers and team members to increase work productivity.	 	\n&bull; 	Gained experience in problem solving and troubleshooting issues for customers.	 	\n&bull; 	Checked client&rsquo;s requests and issues 	regarding the cloud environment.	 	\n&bull; 	Fulfilled the client&#039;s request and resolve the problems while interacting with clients and other internal \ndepartment.	 	\n \nQuest Integr	ity 	Services	, Pune	 	FEB 	202	1 &ndash;Oct 2022	 	\nAssociate System Engineer	 	\n&bull;  Collaborated with 	cross	-functional teams to support troubleshooting and minimize service	 	\ninterruptions.	 	\n&bull; Monitored and documented daily standard service requests for future analysis.\n\n&bull; Created flowcharts, diagrams and other documentation.	 	\nDelivered helpdesk service and support	 to customers.	 	\nProposed technical feasibility solutions for new system designs and suggested options for	 	\n&bull; performance improvement of technical components.	 	\n&bull; Monitored, tracked and prioritized new work requests at Help Desk within set response times.	 	\n \n EDUCATI	ON	 	\n \nNorth Maharashtra University	 	         	2019	 	\nCourse:	 Computer Engineering	, GPA	: 7.42	 	\n \nProject	 	\nAdvanced Q&amp;A Chatbots with DataStax Databases and Vector Embedding	 	\n&bull; 	Overview	: Engineered chatbots that provide accurate, context	-aware answers by 	integrating DataStax 	\ndatabases with vector embedding techniques.	 	\n&bull; 	Technologies	: DataStax Databases, vector embedding algorithms, Hugging Face Spaces.	 	\n&bull; 	Outcome	: Achieved a 40% reduction in the need for human intervention in customer service inquiries, 	\nimprovi	ng response accuracy and user satisfaction.	 	\n 	\nCERTIFI	CATIONS	 	\n \n&bull; 	Google&rsquo;s Data Analyst Professional Certificate.	                                                                                                      	2021	 	\n&bull; 	Azur Data Fundamentals                                                                                                       	                        	             	20	21',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 14:33:28',0),(427,102,76,NULL,NULL,'76_102_700108048669d1c38ee9d6_Lochan_Raj_T_DResume.pdf','Lochan_Raj_T_DResume.pdf','Lo ch an  R aj T  D  \nData  S cie n tis t \nData  S cie n tis t  w it h  3 + ye ars   o f  stro ng  e xp erie n ce  d eliv e rin g  h ig h-q ualit y   B 2B  a n d  B 2C  M L m odels .\nExp erie n ce  in  d eve lo pin g  m ach in e le arn in g  m odels  a n d  c o m pute r  v is io n a p plic a tio ns a n d  s o lu tio ns to\nso lv e  r e al- w orld  b usin ess p ro ble m s \nlo ch an d eva ra ja 0 817@ gm ail .c o m  \n8150888972 \nBen g alu ru  \nlin ke d in .c o m /in /lo ch an ra jt d  \nWORK  E X PER IE N CE \nData  S cie n tis t \nHew le tt-P ack ard  E n te rp ris e  \n12/2 022 -  P re se n t\n, \n  Ben galu ru  \nEn han ce d  a ir p o rt  s e cu rit y  a n d  p asse n g er s c re en in g  a ccu ra cy\nby 9 9%  u sin g  a  Y O LO v7 -b ase d  co lo r  d ete ctio n m odel  fo r\neﬃ cie n t  to p  an d   bo tto m  clo th in g   dete ctio n,  k-m ean s\nclu ste rin g   fo r  do m in an t  co lo r  id en tiﬁ ca tio n,  an d   th e\ndelt a _e _cm c m eth o d f o r p re cis e  c o lo r d ete ctio n.  In te g ra te d\nwit h  t h e S A FR  p la tfo rm  f o r c o m pre h en siv e  p eo ple -t ra ck in g  \nPart n ere d  w it h  c ro ss-f u nctio nal  t e am s ( R eg io n a n d  P ro duct)\nto  d eﬁ ne c lie n t r e q uir e m en ts  a n d  d eve lo p P O Cs le ve ra g in g\nMach in e L e arn in g  m odels   a n d  te ch n iq ues  to  a d dre ss  th eir\nbusin ess c h alle n g es \nLe d   th e  deve lo pm en t  an d   im ple m en ta tio n  of  a  m ed ic a l\ndo m ain   PO C  fo r  tu m or  cla ssiﬁ ca tio n.  A ch ie ve d   a  99.8 %\naccu ra cy   ra te  w it h  a  n ove l  im ag e p ro ce ssin g  m odel  b uil t\nusin g  V is io n T ra n sfo rm ers  \nSp earh ead ed  a  P O C fo r  fra u d  tra n sa ctio n d ete ctio n at  a\nle ad in g  In d o nesia n  b an k.  L e ve ra g ed  O CR  to ols   lik e  T a b ula\nan d   PyP D F2   to   extra ct  stru ctu re d   data   fro m  acco unt\nsta te m en ts , e n ab lin g  r e al- t im e f ra u d  i d en tiﬁ ca tio n \nExp erie n ce   in   m odel  m onit o rin g ,  tra ck in g ,  an d   sto rin g\ndiﬀ ere n t  model  mult ip le -r u n  re su lt s   wit h   diﬀ ere n t\npara m ete rs  o n t h e G CP c lo ud  u sin g  M LF lo w  \nSo ft w are  E ng in eer-L 2  \nPace  W is d o m  S o lu tio ns P vt L td  \n03/2 020 -  1 1/2 022\n, \n  Ben galu ru  \nConstru cte d  a  t a rg ete d  c u sto m er c la ssiﬁ ca tio n m odel  u sin g\nXG Boost  to   pro vid e  actio nab le   busin ess  in sig hts   to   th e\nsta ke h o ld ers  f o r m ark e tin g  a n d  p ro m otin g  t h eir  m utu al f u nd\nan d  i n su ra n ce  p la tfo rm  \nDeve lo ped  a  P O C f o r a  t o uris m -s m art  it in era ry  t o  o ne o f t h e\nto p  10  re n te d   bik e   co m pan ie s  usin g   Fo liu m  to   cre ate\nin te ra ctiv e  g eo gra p hic  v is u aliz a tio ns \nVis u aliz in g   th e  cu sto m er  po rt fo lio   pag e  w it h   diﬀ ere n t\ngra p hs a n d  c h art s  u sin g  S e ab o rn  a n d  M atp lo tlib  \nED UCATIO N \nPost G ra d uatio n D ip lo m a i n  D ata  S cie n ce  \nIIIT -B an g alo re  \n12/2 020 -  0 1/2 022\n, \n  Ben galu ru  \nDeep  L e arn in g  \nBE i n  E le ctro nic s a n d  I n stru m en ta tio n \nMaln ad  C ollg e o f E n g in eerin g  \n2015 -  2 019\n, \n  SK IL L S  \nPyth o n \nMach in e L e arn in g  A lg o rit h m s \nArt iﬁ cia l I n te llig en ce  \nCom pute r V is io n \nDeep  L e arn in g  \nIm ag e P ro ce ssin g  \nSQ L \nKera s \nED A \nOpen C V  \nScik it -L e arn  \nTe n so rF lo w  \nGCP \nMLF lo w  \nDock e r \nNLP  \nData  V is u aliz a tio n -  S e ab o rn , M atp lo tL ib  \nDeb ug gin g  \nDecis io n-M akin g  \nPER SO NAL P R O JE C TS \nMRI I M AG E P R O CESSIN G\n ( 1 2/2 021 -  0 2/2 022)\n \nBuil t  G AN  m odel u sin g  G en era to rs  a n d  D is c rim in ato rs  f o r e xtra ctin g\nmult ip le  i m ag es f ro m  t h e o rig in al M RI i m ag e \nPro je ct  c y cle  in clu d es  D ata  P ro ce ssin g ,  M odel  B uil d in g ,  H an d lin g\nLo ss a n d  M odel T ra in in g  \nACH IE V EM EN TS \nBest P ro je ct A w ard  \nHPE. &bull;  F o r b uil d in g C om pu te r V is io n b ase d  m odel \nSta r P erfo rm er O f T h e Q uart e r \nPace  W is d o m  S o lu tio ns P vt L td  &bull;  2 021-2 022 \nLA N GUAG ES \nEn g lis h  \nFu ll P ro fe ssio nal P ro ﬁ cie n cy  \nKan nad a \nNativ e  o r B il in gual P ro ﬁ cie n cy  \nIN TER EST S \nTe ach in g  \nPho to gra p hy \nAch ie ve m en ts /T a sk s \nAch ie ve m en ts /T a sk s \nCours e s',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 14:33:28',0),(428,102,76,NULL,NULL,'76_102_403970737669d1c38eeb89_Lokesh_Jaiswal_AI_(1).pdf','Lokesh_Jaiswal_AI (1).pdf','',2,3,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 14:33:28',0),(429,102,76,NULL,NULL,'76_102_774637102669d1c38eed5e_Manjunatha_A_Resume_(1).pdf','Manjunatha A Resume (1).pdf','Man ju n ath a A   	A I S o ft w are  E n gin eer	\nBen ga lu ru , 5 60029,	 	m an ju n ath an air m an ju @ gm ail .c o m	 	\n8317321587	 	13/1 1/1 996	 	Lin ke d In	 	G it h ub	 	\nC AR EER  O BJE C TIV E\nI  p osse s 3  y e a rs  in  D ata  S cie n ce ,  I  h ave  d eliv e re d  e n d -t o -e n d  s o lu tio n s in  G en -A I,  M ach in e L e a rn in g,  a n d\nC om pute r V is io n /N LP  p ip elin es.  M y h an d s-o n  e xp ertis e  in  d eve lo p in g a n d  d ep lo yin g a d va n ce d  m od els  d riv e s\nin nova tio n  a n d  i m pactfu l o u tc o m es, o ptim iz in g b usin ess p ro ce sse s w it h  c u tt in g-e d ge  t e ch n olo gie s.\nE d uca tio n\n06/2 018 &ndash;  0 5/2 021\nK alb urg i,	 I n d ia	\nM CA, 	C en tr a l 	U niv e rs it y  	o f 	K arn a ta ka	\nM CA w as c o m ple te d  w it h  a  f ir s t-p la ce  f in is h  a n d  a  g o ld  m ed al, w it h  a n  o ve ra ll s c o re	 	\no f 9 0.9 3% .	\n06/2 015 &ndash;  0 5/2 018\nS h iv a m ogga ,	 I n d ia	\nB CA, 	S a hya dri 	S cie n ce  	C olle g e	\nB CA w as c o m ple te d  w it h  t h e f if t h  r a n k w it h  a n  o ve ra ll s c o re  o f 7 8.1 7%	\nP ro fe ssio n al E x p erie n ce\n08/2 022 &ndash;  p re se n t\nC hen nai,	 I n d ia	\nA I 	S oft w are  	E n gin eer, 	D ete ct 	T e ch n olo g ie s 	P vt 	L td	\n&bull;F o cu se d  o n  e n han cin g w ork sp ace  s a fe ty  i n  c h alle n gin g e n vir o n m en ts .\n&bull;S p ecia liz in g i n  i n te llig e n t v id eo  a n aly tic s, e m plo yin g P yth on , D ock e r, a n d	 	\nFFm peg .\n&bull;Exp ertis e  i n  v id eo /im age  p ro ce ssin g, d ete ctio n , c la ssif ic a tio n , a n d  c o n fin ed  s p ace	 	\nm on it o rin g.	\n09/2 021 &ndash;  0 8/2 022\nB en ga lu ru ,	 I n d ia	\nJ r.D ata S cie n tis t, 	iN eu ro n	\n&bull;W ork e d  o n  c o lle ctin g, c le a n in g, a n d  p re p ro ce ssin g d ata  t o  e n su re  i t s  q ualit y  a n d	 	\nsu it a b il it y  f o r a n aly sis .\n&bull;D eve lo p ed  a n d  i m ple m en te d  m ach in e l e a rn in g m od els  u sin g M L a n d  D L	 	\nalg o rit h m s. T h is  m ay i n clu d e t a sk s s u ch  a s f e a tu re  e n gin eerin g, m od el t ra in in g,	 	\nan d  m od el e va lu atio n .	\n01/2 021 &ndash;  0 6/2 021\nM um bai,	 I n d ia	\nD ata  	S cie n tis t 	In te rn , 	D eve le a rn	\nW ork e d  o n  a  r e a l- t im e f a ce  a tt e n d an ce  s y ste m  p ro je ct. T h e m ain  p urp ose  o f t h e	 	\nR ea l- T im e f a ce  r e co gn it io n  a tt e n d an ce  s y ste m  i s  t o  m ake  t h e a tt e n d an ce  s y ste m	 	\na u to m ate  a n d  e n han ce  t h e q ualit y  o f t h e c u rre n t a tt e n d an ce  s y ste m . I n  o u r s y ste m ,	 	\nwe a re  p ro p ose d  a  m od el w hic h  i s  o n e o f t h e b est l ig h tw eig h t a lg o rit h m s f o r f a ce	 	\nre co gn it io n  w it h  l e ss t im e c o m ple xit y .	\nS kil ls\nM ach in e L e a rn in g	 &mdash;  	Reg re ssio n , C la ssif ic a tio n  a n d  C lu ste rin g	P yth on	D eep  L e a rn in g	 &mdash;  	Com pute r	\nV is io n  a n d  N LP	C om pute r V is io n	 &mdash;  	Im age  a n aly sis  a n d  e n han ce m en t, T ra n sfo rm atio n s, F il t e rin g, F e a tu re	\ne xtra ctio n , a n d  P o se  E stim atio n	W eb App D ev e lo p m en t	 &mdash;  	Fla sk  a n d  D ja n go	C  a n d  C ++	\nTe n so rflo w	P yTo rc h	D ata b ase	 &mdash;  	MyS Q L,P o stg re S Q L a n d  M on go D B	D ock er a n d  G it\n\nPro je cts\n0 3/2 024 &ndash;  p re se n t	S afe S ig h t: 	I n d ustr ia l 	H elm et 	a n d  	P PE 	C om plia n ce  	w it h  	G en  	A I	\nI u n d erto ok a  p ro je ct u til iz in g v is io n -b ase d  l a rg e  l a n gu age  m od els , i n clu d in g G PT-\n4 o , L L A VA , L L A VA N ext, a n d  i D EFIC S. B y t e stin g t h ese  m od els  o n  d iv e rs e  s e ts  o f	 	\nin d ustr ia l i m age s, I  i m pro ve d  p ro m pt a cc u ra cy  t o  a ro u n d  9 5%  f o r d ete ctin g h elm et	 	\na n d  P PE s u it  c o m plia n ce  a n d  v io la tio n s. A d dit io n all y , I  d eve lo p ed  a n  A P I u sin g A W S	 	\nAP I G ate w ay a n d  A W S L a m bd a t o  a u th en tic a te  o b se rv a tio n s b ase d  o n  p ro m pt	 	\nen gin eerin g s ta n d ard s w it h  O pen AI.\nT e ch sta ck	 :  P ro m pt e n gin eerin g,G PT-4 o , L L A VA , L L A VA N ext, i D EFIC S ,  A W S L a m bd a.	\n04/2 023 &ndash;  0 2/2 024	V ig il a n t 	E ye : 	A I- P ow ere d  	W ork p la ce  	S afe ty	\nT h is  p ro je ct e m plo ys f a cia l r e co gn it io n  t e ch n olo gy f o r w ork e r a u th oriz a tio n ,	 	\nm on it o rin g t h e e n vir o n m en t f o r s a fe ty  v io la tio n s l ik e  h elm et o r P PE s u it  a b se n ce s. I t	 	\na ls o  p ro vid es p ers o n aliz e d  w ate r b re a k r e m in d ers , r e a l- t im e a le rts  f o r u n au th oriz e d	 	\nen tr y /e xit , g a s l e ve l/ te m pera tu re  m on it o rin g, e m erg e n cy  a le rts , a n d  c o n tin uou s	 	\nactiv it y  t ra ck in g.\nTe ch  s ta ck	: P yth on , Y o lo , P yT o rc h , d ete ctio n , c la ssif ic a tio n , A m azo n  R eko gn it io n ,	 	\nD ock e r.	\n0 9/2 022 &ndash;  0 4/2 023	C on ta in er 	V ertic a l 	T e xt 	D ete cto r	\nD ete ctin g v e rtic a l t e xt o n  t h e t ra in  c o n ta in er P O C. I  c re a te d  a  w eb ap p u sin g F la sk ,	 	\nexp lo re d  v a rio u s c o m pute r v is io n  l ib ra rie s,, a n d  c re a te d  c u sto m  C N N a n d  L S T M	 	\nm od el t o  d ete ct v e rtic a l t e xt. D ep lo ye d  o n  A W S c lo u d  u sin g E C 2 i n sta n ce .	 	\nC on tin uou s m on it o rin g o f t h e p ro je ct u sin g d ash b oard  o n  A W S a n d  a ls o  o n  A W S	 	\nEC S.\nTe ch  s ta ck	: T e n so rF lo w , P yT o rc h , P an d as, S Q L, L S T M , a n d  D eep  L e a rn in g	\n11/2 021 &ndash;  0 6/2 022	Fa ce  	R eco gn it io n  	B ase d  	Su rv e il la n ce  	S yste m	\nT h e m ain  t a sk  o f t h is  p ro je ct i s  t o  d ep lo y t h e m od el o n  e n d  d evic e s l ik e  t h e J e ts o n	 	\nN an o, J e ts o n  X a vie r, a n d  R asp berr y  P i.  E xp lo re d  v a rio u s q uan tiz a tio n  t e ch n iq ues	 	\nlik e  T FLit e , T a o  C on ve rte r, , P yto rc h  t o  T Flit e . D ep lo ye d  o n  e n d  d evic e s l ik e  J e ts o n	 	\nN an o a n d  J e ts o n  X a vio r\nTe ch  S ta ck	: M TC N N, P o stg re S Q L, c u sto m  d ata	\nA w ard s a n d  A ch ie v e m en ts\n0 8/0 1/2 019	A ce  	o f 	In nova tio n  	Sw arn a 	(Y o u n g 	S cie n tis t) 	a w ard -2 019, 	\nS u vid ha  	In nova tio n  	Tru st	\nS u vid ha I n nova tio n  T ru st h as g iv e n  m e t h e A ce  o f I n nova tio n  a w ard  f o r m y r e se a rc h	 	\nw ork  i n  r o b otic s, m ach in e l e a rn in g, a n d  d eep  l e a rn in g.	\n03/0 8/2 020	S m art 	In d ia  	H ack ath on  	2020 	Fin alis t, 	M HRD	\nPartic ip ate d  i n  t h e 3 D  m od el- b ase d  s y n th esis  r e co gn it io n  o f p en cil  s k e tc h -b ase d	 	\nfa cia l i m age s a n d  s e cu re d  s ix th  p la ce .	\n1 5/0 4/2 019	P re se n te d  	a 	p ap er 	o n  	 &ldquo;V is io n  	base d  	poth ole  	d ete ctio n&rdquo;, 	\nN IT  	W ara ng a l 	a sso cia te d  	by 	TS S C	\nP re se n te d  a  p ap er i n  N IT  W ara n ga l a sso cia te d  b y T S S C  o n  t h e t o p ic  &ldquo; V is io n  b ase d	 	\np oth ole  d ete ctio n&rdquo;	\n22/0 5/2 022	P re se n te d  	a 	p ap er 	o n  	&quot;R ea l-T im e 	Fa ce  	R eco gn it io n  	fo r 	O rg a n iz a tio n al 	\nA tt e n d an ce  	S yste m s&quot;  	 re se a rc h  	pap er 	o n  	R TIP 2R  	co n fe re n ce  	p ub lis h ed  	in  	\nSp rin ge r, 	S p rin g er 	p ub lic a tio n	\nIt  i s  a n  i n te rn atio n al c o n fe re n ce  o n  p att e rn  r e co gn it io n  a n d  i m age  p ro ce ssin g.',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 14:33:28',0),(430,102,76,NULL,NULL,'76_102_1434326025669d1c38eef15_Manjunatha_A_Resume.pdf','Manjunatha A Resume.pdf','Man ju n ath a A   	A I S o ft w are  E n gin eer	\nBen ga lu ru , 5 60029,	 	m an ju n ath an air m an ju @ gm ail .c o m	 	\n8317321587	 	13/1 1/1 996	 	Lin ke d In	 	G it h ub	 	\nC AR EER  O BJE C TIV E\nI  p osse s 3  y e a rs  in  D ata  S cie n ce ,  I  h ave  d eliv e re d  e n d -t o -e n d  s o lu tio n s in  G en -A I,  M ach in e L e a rn in g,  a n d\nC om pute r V is io n /N LP  p ip elin es.  M y h an d s-o n  e xp ertis e  in  d eve lo p in g a n d  d ep lo yin g a d va n ce d  m od els  d riv e s\nin nova tio n  a n d  i m pactfu l o u tc o m es, o ptim iz in g b usin ess p ro ce sse s w it h  c u tt in g-e d ge  t e ch n olo gie s.\nE d uca tio n\n06/2 018 &ndash;  0 5/2 021\nK alb urg i,	 I n d ia	\nM CA, 	C en tr a l 	U niv e rs it y  	o f 	K arn a ta ka	\nM CA w as c o m ple te d  w it h  a  f ir s t-p la ce  f in is h  a n d  a  g o ld  m ed al, w it h  a n  o ve ra ll s c o re	 	\no f 9 0.9 3% .	\n06/2 015 &ndash;  0 5/2 018\nS h iv a m ogga ,	 I n d ia	\nB CA, 	S a hya dri 	S cie n ce  	C olle g e	\nB CA w as c o m ple te d  w it h  t h e f if t h  r a n k w it h  a n  o ve ra ll s c o re  o f 7 8.1 7%	\nP ro fe ssio n al E x p erie n ce\n08/2 022 &ndash;  p re se n t\nC hen nai,	 I n d ia	\nA I 	S oft w are  	E n gin eer, 	D ete ct 	T e ch n olo g ie s 	P vt 	L td	\n&bull;F o cu se d  o n  e n han cin g w ork sp ace  s a fe ty  i n  c h alle n gin g e n vir o n m en ts .\n&bull;S p ecia liz in g i n  i n te llig e n t v id eo  a n aly tic s, e m plo yin g P yth on , D ock e r, a n d	 	\nFFm peg .\n&bull;Exp ertis e  i n  v id eo /im age  p ro ce ssin g, d ete ctio n , c la ssif ic a tio n , a n d  c o n fin ed  s p ace	 	\nm on it o rin g.	\n09/2 021 &ndash;  0 8/2 022\nB en ga lu ru ,	 I n d ia	\nJ r.D ata S cie n tis t, 	iN eu ro n	\n&bull;W ork e d  o n  c o lle ctin g, c le a n in g, a n d  p re p ro ce ssin g d ata  t o  e n su re  i t s  q ualit y  a n d	 	\nsu it a b il it y  f o r a n aly sis .\n&bull;D eve lo p ed  a n d  i m ple m en te d  m ach in e l e a rn in g m od els  u sin g M L a n d  D L	 	\nalg o rit h m s. T h is  m ay i n clu d e t a sk s s u ch  a s f e a tu re  e n gin eerin g, m od el t ra in in g,	 	\nan d  m od el e va lu atio n .	\n01/2 021 &ndash;  0 6/2 021\nM um bai,	 I n d ia	\nD ata  	S cie n tis t 	In te rn , 	D eve le a rn	\nW ork e d  o n  a  r e a l- t im e f a ce  a tt e n d an ce  s y ste m  p ro je ct. T h e m ain  p urp ose  o f t h e	 	\nR ea l- T im e f a ce  r e co gn it io n  a tt e n d an ce  s y ste m  i s  t o  m ake  t h e a tt e n d an ce  s y ste m	 	\na u to m ate  a n d  e n han ce  t h e q ualit y  o f t h e c u rre n t a tt e n d an ce  s y ste m . I n  o u r s y ste m ,	 	\nwe a re  p ro p ose d  a  m od el w hic h  i s  o n e o f t h e b est l ig h tw eig h t a lg o rit h m s f o r f a ce	 	\nre co gn it io n  w it h  l e ss t im e c o m ple xit y .	\nS kil ls\nM ach in e L e a rn in g	 &mdash;  	Reg re ssio n , C la ssif ic a tio n  a n d  C lu ste rin g	P yth on	D eep  L e a rn in g	 &mdash;  	Com pute r	\nV is io n  a n d  N LP	C om pute r V is io n	 &mdash;  	Im age  a n aly sis  a n d  e n han ce m en t, T ra n sfo rm atio n s, F il t e rin g, F e a tu re	\ne xtra ctio n , a n d  P o se  E stim atio n	W eb App D ev e lo p m en t	 &mdash;  	Fla sk  a n d  D ja n go	C  a n d  C ++	\nTe n so rflo w	P yTo rc h	D ata b ase	 &mdash;  	MyS Q L,P o stg re S Q L a n d  M on go D B	D ock er a n d  G it\n\nPro je cts\n0 3/2 024 &ndash;  p re se n t	S afe S ig h t: 	I n d ustr ia l 	H elm et 	a n d  	P PE 	C om plia n ce  	w it h  	G en  	A I	\nI u n d erto ok a  p ro je ct u til iz in g v is io n -b ase d  l a rg e  l a n gu age  m od els , i n clu d in g G PT-\n4 o , L L A VA , L L A VA N ext, a n d  i D EFIC S. B y t e stin g t h ese  m od els  o n  d iv e rs e  s e ts  o f	 	\nin d ustr ia l i m age s, I  i m pro ve d  p ro m pt a cc u ra cy  t o  a ro u n d  9 5%  f o r d ete ctin g h elm et	 	\na n d  P PE s u it  c o m plia n ce  a n d  v io la tio n s. A d dit io n all y , I  d eve lo p ed  a n  A P I u sin g A W S	 	\nAP I G ate w ay a n d  A W S L a m bd a t o  a u th en tic a te  o b se rv a tio n s b ase d  o n  p ro m pt	 	\nen gin eerin g s ta n d ard s w it h  O pen AI.\nT e ch sta ck	 :  P ro m pt e n gin eerin g,G PT-4 o , L L A VA , L L A VA N ext, i D EFIC S ,  A W S L a m bd a.	\n04/2 023 &ndash;  0 2/2 024	V ig il a n t 	E ye : 	A I- P ow ere d  	W ork p la ce  	S afe ty	\nT h is  p ro je ct e m plo ys f a cia l r e co gn it io n  t e ch n olo gy f o r w ork e r a u th oriz a tio n ,	 	\nm on it o rin g t h e e n vir o n m en t f o r s a fe ty  v io la tio n s l ik e  h elm et o r P PE s u it  a b se n ce s. I t	 	\na ls o  p ro vid es p ers o n aliz e d  w ate r b re a k r e m in d ers , r e a l- t im e a le rts  f o r u n au th oriz e d	 	\nen tr y /e xit , g a s l e ve l/ te m pera tu re  m on it o rin g, e m erg e n cy  a le rts , a n d  c o n tin uou s	 	\nactiv it y  t ra ck in g.\nTe ch  s ta ck	: P yth on , Y o lo , P yT o rc h , d ete ctio n , c la ssif ic a tio n , A m azo n  R eko gn it io n ,	 	\nD ock e r.	\n0 9/2 022 &ndash;  0 4/2 023	C on ta in er 	V ertic a l 	T e xt 	D ete cto r	\nD ete ctin g v e rtic a l t e xt o n  t h e t ra in  c o n ta in er P O C. I  c re a te d  a  w eb ap p u sin g F la sk ,	 	\nexp lo re d  v a rio u s c o m pute r v is io n  l ib ra rie s,, a n d  c re a te d  c u sto m  C N N a n d  L S T M	 	\nm od el t o  d ete ct v e rtic a l t e xt. D ep lo ye d  o n  A W S c lo u d  u sin g E C 2 i n sta n ce .	 	\nC on tin uou s m on it o rin g o f t h e p ro je ct u sin g d ash b oard  o n  A W S a n d  a ls o  o n  A W S	 	\nEC S.\nTe ch  s ta ck	: T e n so rF lo w , P yT o rc h , P an d as, S Q L, L S T M , a n d  D eep  L e a rn in g	\n11/2 021 &ndash;  0 6/2 022	Fa ce  	R eco gn it io n  	B ase d  	Su rv e il la n ce  	S yste m	\nT h e m ain  t a sk  o f t h is  p ro je ct i s  t o  d ep lo y t h e m od el o n  e n d  d evic e s l ik e  t h e J e ts o n	 	\nN an o, J e ts o n  X a vie r, a n d  R asp berr y  P i.  E xp lo re d  v a rio u s q uan tiz a tio n  t e ch n iq ues	 	\nlik e  T FLit e , T a o  C on ve rte r, , P yto rc h  t o  T Flit e . D ep lo ye d  o n  e n d  d evic e s l ik e  J e ts o n	 	\nN an o a n d  J e ts o n  X a vio r\nTe ch  S ta ck	: M TC N N, P o stg re S Q L, c u sto m  d ata	\nA w ard s a n d  A ch ie v e m en ts\n0 8/0 1/2 019	A ce  	o f 	In nova tio n  	Sw arn a 	(Y o u n g 	S cie n tis t) 	a w ard -2 019, 	\nS u vid ha  	In nova tio n  	Tru st	\nS u vid ha I n nova tio n  T ru st h as g iv e n  m e t h e A ce  o f I n nova tio n  a w ard  f o r m y r e se a rc h	 	\nw ork  i n  r o b otic s, m ach in e l e a rn in g, a n d  d eep  l e a rn in g.	\n03/0 8/2 020	S m art 	In d ia  	H ack ath on  	2020 	Fin alis t, 	M HRD	\nPartic ip ate d  i n  t h e 3 D  m od el- b ase d  s y n th esis  r e co gn it io n  o f p en cil  s k e tc h -b ase d	 	\nfa cia l i m age s a n d  s e cu re d  s ix th  p la ce .	\n1 5/0 4/2 019	P re se n te d  	a 	p ap er 	o n  	 &ldquo;V is io n  	base d  	poth ole  	d ete ctio n&rdquo;, 	\nN IT  	W ara ng a l 	a sso cia te d  	by 	TS S C	\nP re se n te d  a  p ap er i n  N IT  W ara n ga l a sso cia te d  b y T S S C  o n  t h e t o p ic  &ldquo; V is io n  b ase d	 	\np oth ole  d ete ctio n&rdquo;	\n22/0 5/2 022	P re se n te d  	a 	p ap er 	o n  	&quot;R ea l-T im e 	Fa ce  	R eco gn it io n  	fo r 	O rg a n iz a tio n al 	\nA tt e n d an ce  	S yste m s&quot;  	 re se a rc h  	pap er 	o n  	R TIP 2R  	co n fe re n ce  	p ub lis h ed  	in  	\nSp rin ge r, 	S p rin g er 	p ub lic a tio n	\nIt  i s  a n  i n te rn atio n al c o n fe re n ce  o n  p att e rn  r e co gn it io n  a n d  i m age  p ro ce ssin g.',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 14:33:28',0),(431,102,76,NULL,NULL,'76_102_1689089613669d1c38ef0c1_neel_mishra_resume.pdf','neel_mishra_resume.pdf','Neel Mishra\n+91-9723095555 |GitHub |\nmishraneel99@gmail.com |\nLinkedIn |\nLeetcode |\nExperience Tekion\nBangalore, Karnataka\nData Scientist July 2023 - Present\n&bull; Finetuned open-source LLM models tinyLlama, Llama2, and Zephyr for next email generation, reducing\nexecution time by 98.08% using paged attention and post-training quantization.\n&bull; Validated superior performance of fine-tuned LLMs by conducting advance statistical analysis; achieved21%\nhigher similarity and35% increased relevance in generated emails\n&bull; Constructed a robust RAG question-answering chatbot for vehicular marketing, processing data for 800+ cars\nusing Langchain and advanced features.\n&bull; Streamlined a pipeline to categorize and clean a 16K email dataset, cutting down execution time by 91.7%\nusing multithreading, clustering, and semantic similarities.\n&bull; Engineered a RASAintent classification bot, curating a dataset of 170,000 data points and achieving 96.5%\nvalidation accuracy .\nIIIT Hyderabad Hyderabad, Telangana\nThesis - Applied Numerical Optimization in Deep Learning August 2020 - November 2023 &bull;Collaborated with Principal Applied Scientists fromMicrosoft India on developing a minimax optimizer for\nGANs, achieving top inception scores and cutting execution time to 0.184 seconds .\n&bull; Implemented GPU-compatible PyTorch solvers for Conjugate and BI-Conjugate Gradient methods, achieving over\n96% runtime reduction compared to numpy&rsquo;s standard implementations.\n&bull; Designed a classical minimization solver, enhancing accuracy by up to 2%compared to benchmarks like SGD,\nAdam, and RMSProp on models like Resnet 50, DLA, and VGG.\nEducation International Institute of Information Technology\nHyderabad, Telangana\nMaster of Science by Research in Computer Science, 9.00/10August 2020 - November 2023\nGujarat Technological University Ahmedabad, Gujarat\nBachelor of Engineering in Computer Science, 9.11/10.0 June 2016 - May 2020\nPublications Angle based learning rate for gradient descent\n,Neel Mishra , Pawan Kumar, International Joint Conference on\nNeural Networks (IJCNN 2023, Rank A)link A Gauss-Newton Approach for Min-Max Optimization in GANs\n,Neel Mishra , Pawan Kumar, Bamdev Mishra,\nPratik Jawanapuria, World Congress on Computational Intel ligence (WCCI 2024, Rank A)\nProjects &bull;\nLed a team of four to implement MAP approach for motion deblurring. Utilized gradient descent for blur filter\nestimation and patch stitching for complete ob ject images.[ Link ]\n&bull; Developed a recommendation system using matrix factorization, custom loss formulation for latent factor estimation,\nand L2 regularization to counter overfitting.[ Link ]\nResearch Grants Awards &bull;\nMicrosoft Academic Partnership Grant (MAPG).\n&bull; IIIT &rsquo;s Innovation Hub for Data Banks, Data Services, and Data Analytics (IHub-Data)research grant.\nTechnical Skills Statistical Methods\n: Inferential Statistics, Probability Distributions, Confidence Intervals, p-value Analysis,\nHypothesis testing, Regression Analysis, Correlation Analysis\nMachine Learning : Linear/Logistics Regression, Random Forests, Gradient Boosting, XGBoost, Bagging, SVM, Lasso\nand Ridge and other regularization, Clustering\nLLM : Finetuning, Quantization, Evaluation, Deployment\nRAG : Langchain, LlamaIndex, RASA\nData Analytics : Data Mining, Data Integration, Web Scraping, SQL\nLanguages and tools : Python, Git, Linux, VS Code\nLibraries and Frameworks : PyTorch, TensorFlow, langchain, llama-index, scikit-learn, pandas, numpy',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 14:33:28',0),(432,102,76,NULL,NULL,'76_102_1376279461669d1c38ef2a2_new_2_years_Data_Scientist_Sayali-1_(1).pdf','new_2_years_Data_Scientist_Sayali-1 (1).pdf','SAYALI	 SALUNKHE	 	\nMob:+91	-7769974729	 | Email:	 sayali.sk333@gmail.com	 | Linkedin	 | Github	| Pune,	 India,	 411020	 	\nEXPERIENCE	 SUMMARY	 	 	\n&bull; 	COEP,  Postgraduate 	Data  Scientist  with 	2+  years 	of  overall  experience  focused  expertise  in  the  dynamic  realms  of  Data 	\nScience, specializing in Natural Language Processing (NLP) within the broader spectrum of Machine Learning	 	\n&bull; 	Skilled	 in python	 packages	 like 	Scikit	-learn,	 Numpy,	 Pandas,	 Tensorflow,	 Keras,	 Hugging	 Face,	 NLTK,	 Spacy,	 Gensim, 	\nMatplotlib, Streamlit, Flask.	 	\n&bull; 	Worked  in  projects  involving 	classification,  clustering  and  regression  algorithms  like  KNN,  Decision  trees,  Random 	\nforest, XGBoost, SVM, K	-Means, LSTMs 	and also have	 an understanding of	 CNN&rsquo;s, Embeddings, Prompt Engineering, 	\nWord2Vec,Encoders	-Decoders,Seq2Seq,Attentions,Transformers(BERT),Generative	 AI	 ,ChatGPT,Open	 AI	 API	 with 	\ndeployment in various platforms like AWS (EC2) Sagemaker, GCP	 	\n&bull; 	Comfortable with end to end machine learning lifecycle, starting with	 data collection,	 EDA, feature engineering, feature scaling, 	\nmodel creation, hyperparameter tuning, deployment in production, and retraining the model	. 	\nEDUCATION	 	\nCollege	 of Engineering	 Pune,	 COEP	 Pune	 	July	 2015	 - July	 2017	 	\n&bull; 	Master of Technology: Electronics 	Engineering	 	 CGPA	: 8.96/10 	\nPune Institute of Computer Technology, PICT Pune	 	July	 2010 	- July	 2014	 	\n&bull; 	Bachelor of Engineering: Electronics and Telecommunication	 	 Distinction: 71.6 % 	\nMSBSHSE Board, KTHM College, Nashik	 	July	 2008	 - May	 2010	 	\n&bull; 	HSC	 - Science	 	86.70%	 	\nWORK	 EXPERIENCE	 	\nGenerative	 AI	 Engineer,	 Datamorphosis	 Pvt.Ltd	 	Mar	 24	-May	&rsquo;24	 	\n&bull; 	Collaborated	 with	 the	 backend	 AI	 team	 to develop	 doubt	 clustering,	 question	 answering,	 and	 generative	 AI	 modules.	 	\n&bull; 	Implemented	 NLP	 techniques,	 leveraging	 transformers	 and	 LLMs	 for	 enhanced	 performance	 	\nData	 Scientist,	 Freelancer	 	Sept	 22	-Jan	 24	 	\n&bull; 	Assist	 in the	 design	 and	 development	 of AI	 models.	 	\n&bull; 	Engage	 in data	 analysis	 and	 preprocessing	 for	 machine	 learning.	 	\n&bull; 	Collaborate	 closely	 with	 the	 AI	 team to	 optimize	 algorithms	 and	 augment	 existing	 models.	 	\n&bull; 	Present	 research	 findings	 and	 actively	 contribute	 to team	 meetings.	 	\n&bull; 	Developed	 extractive	 and	 abstractive	 summarization	 using	 advanced	 NLP	 techniques.	 	\n&bull; 	Enhanced	 NLP,	 Python,	 and	 ML	 skills;	 deployed	 Transformer	 models	 (e.g.,	 BERT,	 GPT	-4).	 	\n&bull; 	Used	 metrics	 like	 ROUGE,	 BLEU	 to measure	 and	 refine	 summarization	 quality.	 	\n&bull; 	Teamed	 up with	 cross	-functional	 colleagues,	 shared	 insights,	 and	 findings.	 	\n&bull; 	Enhanced	 summarization	 model,	 presented	 results,	 and	 expanded	 NLP	 expertise.	 	\n&bull; 	Worked	 on	 NLP,	 Computer	 Vision	 and	 Machine	 Learning	 based	 Projects.	 	\nData	 Science	 Intern,	 iNeuron.ai	| Remote	 	Sept	 19	 - Sept	 20	 	\n&bull; 	Requirement	 Gathering	 &amp; Translation:	 Gathering	 data	 from	 various	 sources,	 collecting	 our	 customer&#039;s	 data,	 and	 translating	 it 	\ninto use cases to solve real	-world problems.	 	\n&bull; 	Data Cleansing, Statistical &amp; Data Analysis: Clean, transform &amp; extract insights and provide sentiment analysis using the	 	\nfeedback from the courses for providing data	-driven recommendations.	 	\n&bull; 	Training &amp; Implementation: Developing content	-based course recommendation systems and an automated chat system for 	\nquestions and answers. Creating machine learning models as well for various use cases supplied by the clients	 	\n&bull; 	Knowledge	 in building	 applications	 with	 Artificial	 Intelligence,	 Machine	 Learning,	 Deep	 Learning,	 Recurrent	 Neural 	\nNetwork, Computer vision, NLP and Python.	 	\n&bull; 	Worked on Deployment and optimization of various projects computer vision, Machine learning and NLP solution on web	- 	\nbased application, Azure platform, AWS, GCP.	 	\nMachine	 Learning	 Engineer,	 Cognizant	 Technology	 Solutions	 | Pune,	 India	 	Feb	 18	 - Aug	 19	 	\n&bull; 	Data	 Analytics	 and	 Reporting:	 	\n- Explore	 and	 examine	 data	 from	 multiple	 disparate	 sources.	 Prepare	 a data collection	 plan	 from	 both	 structured	 and 	\nunstructured sources Collaborate and co	-ordinate with Technology and Business teams for all data needs.	 	\n- Perform	 exploratory	 data	 analysis	 and	 generate	 insights	 Validate	 hypothesis	 developed	 during	 exploration	 phase	 Present 	\ninitial results to business stakeholders and identify the next steps.	 	\n&bull; 	Create	 models	 using	 Python	 which	 involve	 Classification,	 Clustering.	 	\n&bull; 	Deriving	 insights	 and	 recommendations	 from	 the	 models.	 Performing	 data	 visualization	 and	 presentation	 to clients.\n\n&bull; 	Document	 all modeling	 steps	 in a systematic	 way	 including	 modeling	 process,	 insights	 generated ,	 presentations	 , model 	\nvalidation results and checklists built in the project.	 	\n \nTECHNICAL	 SKILLS	 	\nProgramming	 Languages	: (Python)	 | Frameworks	 (Tensorflow	 | PyTorch)	 | Rest	 API	 (Flask	 | FastAPI)	 	\nNatural	 Language	 Processing	 (RNN	 | Text	 Preprocessing	 | Transformers	 | BERT	 | Encoder	-Decoder	 | NER	 | Spacy	 | NLTK)	 	\nDatabase	 (MySql	 | MongoDb	 ) 	\nCloud	 (AWS,	 AZURE)	 | MLOps	 (Git	 | Docker	 | Github	 Actions)	 	\nMachine	 Learning	 Algorithms	 (Linear	 Regression	 | Logistic	 Regression	 | Decision	 Tree	 | Random	 Forest	 | Xg	 Boost) 	Feature 	\nEngineering 	(Pandas | Numpy)	 	\nLarge	 Language	 Models	 (Prompt	 Engineering	 | LangChain	 | Vector	 DB	 | OpenAI	 | LLM)	 	\nPROJECTS	 	\nChatbot	 Answering	 from	 Your	 Own	 Knowledge	 Base:	 Langchain,	 ChatGPT,	 Pinecone	 and	 Streamlit	 	\n&bull; 	Developed	 a chatbot	 that	 leverages	 the	 power	 of Langchain,	 OpenAI&#039;s	 ChatGPT,	 Pinecone,	 and	 Streamlit	 Chat.	 	\n&bull; 	Developed	 chatbot	 not	 only	 responds	 to user	 queries	 but	 also	 refines	 queries	 and	 pulls	 information	 from	 its own	 document 	\nindex, making it efficient and capable of answering in	-depth questions.	 	\nAutomated	 Document	 Summarization	 using	 NLP	 	\n&bull; 	Developed	 a document	 summarization	 parser	 using	 TensorFlow	 and	 T5	-small	 HuggingFace	 pre	-trained	 model.	 	\n&bull; 	Text	 data	 from	 PDF	 is extracted	 using	 the	 PyPdf	 library.	 	\n&bull; 	Evaluated	 the	 performance	 of the	 summarization	 algorithm	 using	 metrics	 such	 as ROUGE	 and	 BLEU	 scores.	 	\n&bull; 	Conducted	 A/B	 testing	 to evaluate	 the	 performance	 of the	 algorithm	 against	 other	 state	-of-the	-art	 summarization	 techniques.	 	\n&bull; 	Utilized	 AWS	 cloud	 for	 project	 implementation	 and	 EC2	 instance	 for	 model	 deployment.	 	\n&bull; 	Implemented	 CI/CD	 using	 Github	 actions.	 	\nVideo	-Document	 based	 Conversational	 AI	 system	 using	 LLM	 and	 LangChain	 	\n&bull; 	Built	 a conversational	 AI	 system	 utilizing	 LangChain	 and	 LLM	 to extract	 key	 information	 from	 video	 transcripts	 or 	\ndocuments.	 	\n&bull; 	Whisper	 Base	 7b	 is used	 for	 audio	 transcription.	 	\n&bull; 	Used	 OpenAI	 embeddings	 class	 for	 generating	 the	 embeddings.	 	\n&bull; 	Used	 Chroma	 DB	 to save	 the	 embeddings	 and	 cosine	 similarity	 is used	 to find	 out	 the	 similar	 chunks	 of documents.	 	\n&bull; 	A group	 of tasks	 including	 video	 summarisation,	 and	 chat	-with	-video/documents	 are	 obtained	 using	 text	-davinci	-003	 LLM 	\nmodel	 	\nFine	-Tune	 a Generative	 AI	 Model	 for	 Dialogue	 Summarization	 	\n&bull; 	Fine	-tune	 an existing	 LLM	 from	 Hugging	 Face	 for	 enhanced	 dialogue	 summarization	 	\n&bull; 	Set	 the	 FLAN	-T5	 model,	 which	 provides	 a high	-quality	 instruction	 tuned	 model	 and	 can	 summarize	 text	 out	 of the	 box.	 	\n&bull; 	Perform	 Parameter	 Efficient	 Fine	-Tuning	 (PEFT	), evaluate	 the	 resulting	 model	 and	 see	 that	 the	 benefits	 of PEFT	 outweigh 	\nthe slightly	-lower performance metrics.	 	\nTRAINING	 AND	 CERTIFICATIONS	 	\n&bull; 	Certified	 Data	 Science	 Professional,	 OdinSchool	 Certificate	 ID	-O4gQIBA4O0	  	Feb	 2023	-Sept	 2023	 	\n&bull; 	Generative	 AI	 with	 LLM	 	\n&bull; 	Microsoft	 Azure	 for	 Data	 Engineering	 	\n&bull; 	5 star	 Hacker	 Rank	 Profile	 	\n&bull; 	Testdome	 Python	 and	 SQL	 skill	 test	 certifications	 	\n&bull; 	Google	 Generative	 AI	 Learning	 Path	 	\n&bull; 	Graduate	 Level	 course	 from	 Stanford	 cs224n	: Natural	 Language	 Processing	 	\n&bull; 	GATE	 2015	 Qualified',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 14:33:28',0),(433,102,76,NULL,NULL,'76_102_757172224669d1c38ef49b_Ranjith_Kumar_KN_2024_(2).pdf','Ranjith_Kumar_KN_2024 (2).pdf','R	a n j i t h   K u m a r	 K.	N	. 	\n  Bengaluru	, Karnataka	, Indi	a         	 LinkedIn/Ra	njith	-Kumar	   	  GitHub/R	anjith	-Kumar	          	 +91	 90087	69944	 	   ranjith.go226	0@gmail.com	 	\nE x p e r i e n c e	 	\nData Engineer	 | Pravaig 	Dynamics |	 Bengaluru, 	Karnataka,	 India	                                                                       	          	February 202	3 - Prese	nt      	                                       	\n&bull; 	Spearheaded  data  engineering  initiatives  within  the  Electric  Vehicle  Research  &amp;  Development  team	 and 	ensure  seamless  integration  of  data  engineering 	\nsolutions with EV R&amp;	D, enabling informed decision	-making based on data insights.	 	\n&bull; 	Developed and put into use data pipelines for vehicle data collecting that make effective use of technologies such as Kafka.	 	\n&bull; 	Integrat	ion of CAN	-BUS, Radar,	 GPS and camera data into the data processing workflow, optimizing data ingestion and analysis	. 	\n&bull; 	Leveraged ROS2 framework for processi	ng IMU data, ensuring seamless integration and compatibility within the data ecosystem.	 	\n&bull; 	Implemen	ted and maintained automated data processing workflows using Jenkins, enhancing overall efficiency and reducing processing ti	mes.	 	\n&bull; 	Managed AWS infrastr	ucture  to support scalable data storage and  computation needs for EV data analytics.	 Developed and maintained an  in	-house data 	\nwarehouse to centralize and optimize data storage, retrieval, and analysis.	 	\n \nBig Data	 Engineer (	Apprenticeship)	 | T	ata	 Consultancy	 Services	 |Bengaluru, 	Karnataka,	 India	            	                  	April	 2022 	&ndash; October 	2022	 	\n 	\n&bull; 	Developed  and  maintained  scalable  data  pipelines  using  Hadoop,  Spark,  and  oth	er  Big  Data  technologies  to  process  large  volumes  of  data,  resulting  in 	increased  efficiency  by  30%.	 Conducted  data  analys	is  and  visualization  to  extract  actionable  insights,  aiding  stakeholders  in  making  informed  business 	decisions.	 	 	\nResearch Scholar 	- Data Scienc	e | MENMOZHI 	TECHNOLOGIE	S |Bengaluru	, Karnataka,	 India	             	                 	 February	 2022 	&ndash; May	 2022	 	\n 	\n&bull; 	Experience  in  the  space  of  Software  Development  in  Computer  Vision	 utilizing  a  m	ix  of  customary  Image  Processing  based  and  current	 Machine 	Learning/Deep Learning	-based methods.	 	 	\n Skills	 \n&bull; 	Programming	 Skills	: Python	 (Pandas,	 NumPy,	 Matplotlib	), Machine	 Leaning	, Deep	 Leaning	 (CNN),	 Tensor	Flow, PyTorch	, Deep Leaning, Scala,	 	\nR Langua	ge. 	\n&bull; 	 	o	 	 HTML, REAC	T, CSS, DJANGO, FLASK, FAST 	API.	  	\n&bull; 	Database	 Skill	s: SQL,	 MongoDB,	 Redis	, Hadoop	, Hive	, NoSQL.	 	\n&bull; 	Miscellaneous	 Skills	: Git,	 Docker	, Applied	 Statistics,	 Power	 BI, Jenkins	,  Circle  CI	, Rabbi	tMQ	, Kafka, Ansi	ble	, Airflo	w, ML	-flow, 	AWS	, Azure, 	\nMu	ltiprocessing, Statistical Modeling, Hypothesis Testing	, Clustering	, ETL, 	Object Detection, NLP, NMT	. 	\n&bull; 	 	Time 	Ma	nagement, Teamwork, Proble	m-solving, Docum	entation, Scum	, Meeting and Engaging Presentation. 	\nE d u c a t i o n	 	\nSt. Joseph&#039;s	 University	, Bengaluru	                                                                              	                            	Sheshadripuram	 Collage	, Bengaluru	 	MSc,	 Big Data	 Analytics	, 2020	 &ndash; 2022	       	                                                                                              	Bachelor	 of Computer	 Applications	, 2016	 &ndash; 2019	 	\nP r o j	e c t s	 	\nA study on dog emotion recognition using deep learning tec	hniques	 (AIP (American Institute of Physics)	 Recognized):	 	\nThe purpose of this research was to determine if a deep learning system can 	classify	 various	 facial emotion classes in dogs, such as happiness,	 	sadness, anger, and neutral. For	 this, we proposed three different convolutional net	works. The findings prove that, when	 compared to the other models, the suggested 	\nmodel MBCC	-CNN outperforms in terms of	 facial expression recognition in dogs	. Link	 to paper	:  AIP	 Paper	 link	 	\n \nNeural	-Machine	-Translation: Models: Transformers, Bert, SMT, Seq2Seq	: 	\n \nNatural machine translation refers to the process of using computational algorithms and artificial intelligence to translate	. I have used 	BERT, Seq2Seq, and Transformers	 	models	 which	 all are 	significant models in the field of natural language processing (NLP), particularly in machine translation.	 In practice, these models can be combin	ed 	\nor used in different parts of a machine translation system	.  Project	 link	:  NMT	 Proj. link	 	\n C e r t i f i c a t e s	 	\n&bull; Python Data Structures Coursera 	-- Sept 2021: 	 Link                                            	           	 &bull;  Data Analytics Virtual Experience Program	: Link	 	\n&bull; The Sparks Foundation Virtual Internship 	-- October 2021:	  Link                      	           	  &bull;  Data Science	 using	 R: Link',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 14:33:28',0),(434,102,76,NULL,NULL,'76_102_720473241669d1c38ef681_Resume_(7).pdf','Resume (7).pdf','Priya Gupta\n� +91 9455905525\n#priyag239@gmail.com �\nLinkedIn Ob jective\n&bull;\nExperienced data scientist adept at utilizing a diverse skill set encompassing programming languages, tools, and\nmethodologies to drive data-driven solutions. Seeking an opportunity with a progressive organization where I can\ncontribute my expertise in areas such as machine learning, data analysis, and software development to address complex\nchallenges and drive business success\nWork Experience Sravathi AI Pvt Ltd\nMarch 23 &ndash;Present\nData Scientist Bangalore\n&bull; Performed an exhaustive Exploratory Data Analysis (EDA) to meticulously uncover the dataset&rsquo;s underlying\ncharacteristics and patterns. Utilized a combination of advanced visualization tools and robust statistical methodologies\nto unveil significant trends, correlations, and anomalies, thereby establishing a robust framework for subsequent\nanalytical endeavors.\n&bull; Applied advanced feature engineering techniques to preprocess data, refining it for modeling purposes and amplifying\nthe predictive prowess of the models. Leveraged specialized chemistry-related libraries to extract domain-specific\ninsights, ensuring robustness in predicting both reaction outcomes and impurity formation.\n&bull; Utilized Deep Learning methodologies to craft predictive models customized to the dataset, enabling accurate\nforecasting of reaction outcomes. Executed rigorous model training protocols to enhance performance and precision.\nIncorporated considerations for product form variations during reactions and impurity levels to ensure comprehensive\npredictive capabilities\nSravathi AI Pvt Ltd March 22 &ndash; March23\nData Scientist Bangalore\n&bull; Conducted thorough Exploratory Data Analysis (EDA) to gain comprehensive insights into the dataset&rsquo;s characteristics\nand patterns. Leveraged visualization and statistical techniques to uncover key trends, relationships, and outliers, laying\na solid foundation for subsequent analytical tasks\n&bull; Performed feature engineering techniques to prepare data for modeling, enhancing the predictive capabilities of the\nmodels. This strategic approach facilitated\n&bull; Designed and developed predictive models tailored to the dataset to forecast yield outcomes. Implemented model\ntraining processes to optimize performance and accuracy. These efforts streamlined decision-making processes and\ncontributed to increased efficiency in research and development initiatives\n&bull; Visualizing and analyzing data enables inference generation, empowering chemists working in labs to guide their research\nand development efforts more efficiently, thereby saving valuable time\nRBG Labs IIT-Madras Oct2020 - Aug2021\nData Science Research Intern IIT-Madras\n&bull; Conducted statistical analysis to understand data patterns and relationships; identified trends and insights.\n&bull; Developed and implemented deep learning models optimized models for accuracy and performance through\nhyperparameter tuning and validation techniques.\n&bull; Spatial Analysis using the R, python and Deep Learning.\n&bull; Collaborated with research scientists, engineers, and other team members to achieve pro ject goals and reviewed latest\nresearch in data science and machine learning; experimented with new algorithms and methodologies to enhance existing\nprocesses\n&bull; Used: Ob ject Detection Algorithm, Image Analysis,Computer Vision To detect Traffic Rules Violation\n(Overloading),Python and R\n\nAcademic pro ject\nSentiment Analysis\nWeather Forecasting in Time Series Analysis\nDetection of road boundary of curved and muddy road Using Image Processing\nMedical Domain(Covid 19) question answering system Using NLP\nEye gaze estimation using CNN\nCovid-19 analysis in different States or Union Territory using SVM\nLand cover classification using meta learning techniques\nLoan Approval prediction using Logistics Regression\nPredicting Pulsar Star Using Deep Learning\nSpace Exploration Using Reinforcement Learning\nHybrid Approach For Traffic Rules Violations Detection\nUSA Crimedata Analysis using hadoop map-reduce framework\nTechnical Skills Languages\n: Python, R\nLibraries : Numpy, Pandas, Scikit-Learn, Statsmodels, Pytorch, Keras,\nTensorflow,Bert,Biobert,Gensim,Spacy,NLTK,Opencv,Matplotlib,Beautiful Soup,Seaborn\nAlgorithms : Data Visualization, Machine Learning, Deep Learning, Time Series, Computer vision, Image Processing,\nOb ject Detection, YOLO, Text Analytics\nDatabases : Apache Pig,Apache,Hive,Hadoop SQL\nTools : weka, Data Preparator,Labelimg,Eclipse,matlab,Netbeans\nEducation Amrita Vishwa Vidyapeetham M.Tech Data Science\nGrade : First Class\nDr APJ Abdul Kalam University Former UPTU B.Tech in IT\nGrade : First Class\nPublications and Conferences &bull;\nICCNCT-2022\nCertificates and Training &bull;\nC.NET Web Development from Microsoft Training Associate\n&bull; Advanced Java from NIIT AWS Webinar on &rdquo;Insights into AI and ML&rdquo; Workshop on Android and Ethical Hacking\n&bull; Coursera Deep Learning',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 14:33:28',0),(435,103,76,NULL,NULL,'76_103_1547583597669d5282a68de_Devops_Resume_(1).docx','Devops Resume (1).docx','\r\nRESUME\r\nN.B.M.Sai Kishore\r\nContact no:7702806342\r\nEmail Id: ksai93654@gmail.com\r\n Professional Summary:\r\nOver all 2 years of experience as a DevOps Engineer, Extensively hands-on technical experience in the design and creation of large scale distributed and fully automated Continuous Integration and Build &amp;amp; Deployment processes for numerous Agile projects, spanning many different technologies and platforms.\r\nSummary of Experience:\r\n\r\nExperienced in Build, Release, and SCM and Deployment activities.\r\nWorked on Hudson, Jenkins for continuous integration and for End to End automation for all build and deployments.\r\nExtensive experience using MAVEN as build tools for the building of deployable artifacts (jar, war ) from source code.\r\nBuild, Configure, Manage and Coordinate all Build and Release Management activities.\r\nTroubleshooting and problem solving of Linux/UNIX servers, debugging OS failure.\r\nExperience in working on source control tools like Tortoise, Git lab .\r\nHands on working experience in Unix/Linux and Windows Environments.\r\nExpert in deploying the code through web application servers like Apache Tomcat.\r\nAbility in development and execution of Groovy Script.\r\nHaving strong Automation skills with respect to Build/Deploy process.\r\nExperience in working with Tracking Tools JIRA.\r\nExperience on Subversion- Created branches, tagging providing access Permissions over the Branches.\r\nPrepared deployment plans for each deployment of the product.\r\nMaintaining Repository and New user creation/user deletion and set up archive with groups and roles.\r\nExperience in JENKINS to schedule a job as per the requirement, report monitoring and notification functionality to report success or failure.\r\nAbility in managing all aspects of the software configuration management process including code compilation, packaging / deployment / release methodology, and application configurations.\r\nExposure in Agile Methodology Architecture using Java and Oracle as backend Work with Development Teams and Testing Team to establish a build schedule, Execute the builds\r\nProfessional Experience\r\n\r\nWorking as a Devops Infra Engineer in Oracle(OFSS)  June 2022 to Till Date\r\nSkill Set\r\n\r\nOperating Systems Windows, Linux\r\nBuild tools Maven\r\nCI Tools Jenkins, Hudson\r\nApplication Server Weblogic, Apache http\r\nSCM Tools GIT, Bit bucket\r\nCode Quality Analysis SonarQube.\r\nBug/ Project Tracking Tools Jira\r\nCloud Platform AWS\r\nScripting Groovy\r\nContainerization Tool Docker\r\nConfiguration management Tool Ansible\r\nIAAC(Infrastructure as a code) Terraform\r\nArti factory Jfrog, Nexus\r\nProjects:\r\n\r\n              Project 1\r\n   Designation: Devops Infra Engineer\r\n   Environment: Git, GitHub,Tortoise SVN, Jenkins, Apache, Weblogic, Oracle DB, Docker.\r\nDescription\r\n                 Oracle Banking Payments (OBPM) serves as an enterprise payment solution, acting as a centralized\r\n              processor for both Corporate and Consumer payments within Banks. In the back-office, it functions\r\n              as a single-point connection to Payment Clearing Networks that Banks participate in. With built-in \r\n              support for processing Cross-border SWIFT, SEPA, and other Payment Networks in the US, India, \r\n              China, OBPM is equipped to handle diverse international transactions. In the front office, a well-\r\n              integrated DX layer, meeting modern digital banking requirements seamlessly, complements it. \r\n              Designed as a multi-country solution, OBPM can integrate with multiple DDA systems in a single\r\n              instance. Additionally, to fulfil the demands of a payment-processing hub, it can integrate with \r\n             various systems including Sanctions screening, AML, Treasury, Pricing, and Accounting. Upstream \r\n             integration is also possible with channels and other product processors at Banks. SEPA messages\r\n             migrated to the ISO 2019 version to enhance interoperability, standardization, and efficiency within \r\n             the Single Euro Payments Area (SEPA), aligning with global standards to improve communication \r\n             and transaction processing across financial institutions, ultimately boosting the reliability and \r\n             effectiveness of cross-border payments within the Eurozone.\r\nRoles and Responsibilities\r\nResponsible for creating repositories, teams, add members to teams and also providing access to repositories in Git Hub.\r\nSupport the code builds by integrating with continuous integration  tool(Jenkins).\r\nSupporting different environments like Prod, Dev and Staging.\r\nManaging Continuous Integration and Continuous Delivery (CI/CD) using Jenkins.\r\nWorking closely with dev team to manage CI environment using Jenkins, Code Commit.\r\nAutomating Build related tasks using scripting language like Shell.\r\nInvolved in Build and Deployment activities.\r\nInvolved in the release process and deployed applications (WAR and JAR) to the Tomcat.\r\nWork closely with Development team on Build and Deployment issues\r\nUser &amp;amp; Group administration and resolving user related problems.\r\nCreating and monitoring various Jenkins job.\r\nCreating and maintaining state less and state full applications using Docker.\r\nInstalling packages using RPMs &amp;amp; YUM Installation.\r\nConfigure SSH (Secure Shell) and Creating YUM Repository.\r\nAutomation of jobs through Crontab.\r\nMonitoring System components such as CPU, Disk and SWAP space.\r\nConfigured NFS file systems and share them to clients.\r\nConfiguring FTP Server to maintain the shared folders within the Organization.\r\nExperienced with working in Production, Development and Testing environment.\r\nHelp Development teams automate the environment setup using tools like Ansible. \r\nMaintained Playbooks and Inventory for Configuration Management using Ansible.\r\nKnowledge on shell scripting\r\nKnowledge on java \r\nDeploying and monitoring Kubernetes pods and debugging various issues.\r\nKnowledge on Amazon Web Services (AWS), Creating EC2 Instances and configuring all\r\nnecessary services.\r\nDesign and implemented Security using VPC, IAM, Security Groups etc...\r\nInvolved in designing and deploying a multiple application almost all of the AWS stack like EC2, \r\nEBS, S3, RDS, SNS, VPC, IAM, etc. focusing on high-availability, Disaster recovery\r\nConfigured Connection Pool and Data Source Object for Database Connectivity.\r\nCreated and configured the Data sources for database connectivity\r\nConfigured JVM arguments, JMS/JDBC adapters, MQ, RAC data sources, user authentication and troubleshot issues with process deployments.\r\nConfigured Node Manager in a clustered environment &amp;amp; assigning the node manager to various WebLogic server components for remote administration.\r\nConfigure node manager, associating managed servers to a machine to control/monitor managed servers on various physical boxes.\r\nAutomated WebLogic domain configuration and services configuration using Unix Shell scripts.\r\nEnabled session sharing and management across different web applications, domains and heterogeneous application servers using Oracle Coherence.\r\nExperience on PowerShell scripting.\r\nExperience on Oracle db\r\nManage and building Data Guard Standby databases and manage/monitor database backup/recovery and creation/maintenance of development, system test and production database.\r\nPerformed installation and configuration of Database software, Listener, and Database creation in development, staging and production environment.\r\nEducation\r\n\r\nB.Tech in Mechanical Engineering, Passed with 62.2% from Srinivasa Institute of Engineering and Technology.\r\nIntermediate, Passed with 79% from State Board from Narayana junior college.\r\n10th standard, Passed with 75% from State Board.\r\nAcknowledgement\r\n\r\nI hear by declare that all the details furnished above are true to the best of my knowledge.\r\nPlace: Bangalore\r\nDate:\r\n                                                                                                                                                          ----------------------------\r\n',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 18:25:06',0),(436,103,76,47,NULL,'76_103_838322871669d5282a6f39_Resume_Rishav.pdf','Resume Rishav.pdf','Summary	 	\n&bull; 	Having 	5 years	 of  industry  experience  in	 Machine  Learning,  AI, 	Generative  AI,  and  data	 with  few  y	ears  of 	\nexperience in managing	 Data	 science	 and Machine learning	 projects	. Application to the disciplines of machine 	\nlearning  and  neural  networks  using  a  variety  of  systems  and  methods  in  trainin	g  algorithms  with  different 	\nclou	d platform	. 	\n&bull; 	Efficient in preprocessing	 data	 including	 Data	 cleaning, Correlation analysis, Imputation, 	Visualization, Feature 	\nScaling and Dimensionality Reduction techniques using Machine learning platforms like Python	 Data	 Science 	\nPackages (Scikit	-Learn, Pandas, NumPy).	  	\n&bull; 	Experience in 	building	 Natural Language Processing, Text Classification, Semantics and	 Sentiment Analysis	. 	\n&bull; 	Experience  in  Text  Analytics,  developing  different  Statistical  Machine  Learning,	 Data	 Mining  solutions  to 	\nvarious business problems and generat	ing	 data	 visualizations using	 Python.	  	\n&bull; 	Experience in	 data	 analysis, handling missing	 data, 	feature scaling, outlier analysis and 	unstructured data.	 	\n&bull; 	Experience in AI and Deep learnin	g platforms such as 	pytorch, 	TensorFlow 	and Keras.	  	\n&bull; 	Highly competent at researching, visualizing and analyzing raw	 data	 to 	identify recommendations for meeting 	\norganizational challenges.	  	\n&bull; 	Experience in 	EDA, 	building recommended systems	 and working on unstructured data	. 	\n&bull; 	Experience working with R	DBMS such as SQL Server, MySQL	, Oracle	. 	\n&bull; 	Experience in building large 	language model applications	 (LLMs)	 with LLAMA and GPT	, Claude	, and finetunning 	\nfor our own dataset with RAG	 framework	 and 	PEFT technique like 	LORA	. 	\n&bull; 	Experience in working on various vector databases ChromaDB, Pinecone and similarity search with FAISS	 	\nExperi	ence:	 	\nStatusNeo	 Technologies	, Gurugram	 	\nAI/ML Senior Consultant	  	\nDecember 2023	 to Present	  	\nResponsibilities: 	 	\n&bull; 	Responsible for developing chatbot using large language model that answers questions from the	 json 	files	, 	\nfor this	 use case	 we use	 Anthropic	 Claude 	model	 from AWS Bedrock and creating vectorstore wrapper to 	\nretrieve documents	. 	\n&bull; 	Developed	 large language model (LLM) application 	for Sentiment Analysis (3 categories) and Emotion 	\nExtraction (12 categories) to find the best suited sentiment and emotion of the 	complete text. For this use 	\ncase w	e use LLAMA2	-70B LLM model from AWS Bedrock.	 	\n \nBravura Solution	s,  Gurugram	 	\nMachine Learning Engineer 	 	\nSeptember 2022 to 	December 2023	  	\nResponsibilities: 	 	\n&bull; 	Responsible for developing chatbot	 using large language model	 that answers questions from the confluence 	\npages about the projects, for this we use llama	-2 13B model and finetune it with RAG and lora.	 	\n&bull; 	Developing large language 	model (LLM) application for writing SQL	 queries for	 our databases, train acco	rding 	\nto them 	using	 different version of	 llama models	. 	\n&bull; 	Responsible for creating dashboard on our databases insights after EDA and extracting required \ninformation&rsquo;s for our business need.	 	\n&bull; 	Responsible for applying torchs	erve on pytorch trained models which was previously 	with Flask, testing and 	\ndeployment of TorchServe	 on AWS cluster.	 	\n&bull; 	Develop and create	 python package for the product, building pipeline an	d publish own package.	 	\nRishav Kumar	 	\nEmail:	 rishav.adiran@gmail.com	 	\nMobile Number: 	8709145557/9457178931	 	\nlinkedin.com/in/rishav	-kumar	-98b04213b\n\nNCR Corporation	, Gurugram	 	\nData Scientist	 II 	\nMarch	 2021	 to September 2022	 	\nResponsiblities:	 	\n&bull; 	Responsible for building 	Deal Scoring Project	 where 	price range 	of a deal is calculated, it depends on type of 	\ndeal. 	We  are 	four	 people  team	 where	 my  role  here  is  to  write  logic,  API  code  for  FastApi,  connection  with 	\noracle and hive 	tables	 and deployment on 	Linux	 server	. 	\n&bull; 	Developed 	Fraud Detection m	odel 	and fraud analysis of the data 	to find frauds happening in bank transactions 	\ntried several algorithms like	 Anomaly detection, C	lustering 	and Neural Network with feature 	engineering and	 	\nseveral pre	-processing steps	 in Jupyter Notebook through Azure Machine Learning Studio	 instance	  	\n&bull; 	Responsible  for  the  analysis  of 	unstructured 	data  for  Shipment  Consolidation  project  where  objective  is  to 	\nreduce cost of shipment, which is all over	 America	. There are 	two	 parts in this project, first is to how to fit all 	\nthe  packages  inside  a  container 	or  truck 	so  maxi	mum  packages  can  be  uploaded,  second  part  is  of  route 	\noptimization like if a truck is delive	ring several packages for different location. We are five people team where 	\nmy role is of route optimization and information of how packages are organized.	 	\nBeahead	 PVT LTD,	 Noida	 	\nMachine Learning Engineer	 	\nAugust	 201	9 to 	March 2021	 	\nResponsiblities:	 	\n&bull; 	Applied  various  machine  learning  algorithms	,  deep  learning  algorithms	 and  advanced	 statistical	 analysis	 like	 	\nclassification algorithms, regression models using sci	kit	-learn	, keras, tensorflow	 package in Python.	  	\n&bull; 	Build  various  scrapers  for  client  of  Instagram,  Facebook,  Twitter 	and  for  data  mining.  In  the  same  project 	\ngender classification and age group classification model has been build with deep learning algorithms like CNN \nand transfer lear	ning.	 	\n&bull; 	Engineered  seve	ral  models  have  been  built  like  Age  classification  (accuracy 	&ndash; 92%),  Gender  classification 	\n(accuracy 	&ndash; 94%),  Sentiment  analysis  (accuracy 	&ndash; 98%)  and  keep  improving  these  models  to  make  more 	\ninformed decision for the client to find the perfect influencers f	or their brands to advertise. 	 	\n&bull; 	Analysing  the  scraped  influencer  like  how  their  followers,  following,  posts  or  tweets  are  increasing  or \ndecreasing, what kind of posts they are posting, how it is effecting their reach and engagements.	 	\n&bull; 	Responsible for 	building sentiment analysis model	. 	\nTechnical Skills:	 	\nDatabase	: SQL	, MySQL	 	\nProgramming Languages:	 Python	 	\nMachine Learning:	 NLP,	 Regression, Clustering	, Classification	, Transformers, Seq2Seq, 	Dimensionality Reduction, 	\nNatural Language Processing, Topic 	Modelling, Q&amp;A System, Text Summarizer	, Computer vision	 etc	. 	\nGen AI	: , LLMs, Generative AI, ChatGPT, Prompt engineering, Langchain	, HuggingFace, LoRa, 	GPT models, LLAMA, 	\nCLAUDE, FAISS, RAG, Vector Databases, Pinecone, ChromaDB	 	\nTechnologies/Tools:	 Azure Mac	hine Learning	 Studio	, Tensor	flow	, T	orch, Keras	, openCV, 	OCR, pytesseract, 	NLTK, 	\nSpacy, R	egex, BeautifulSou	p, Statsmodel, TorchServe, AWS	 (Sagemaker, Bedrock, Athena, S3bucket)	, Docker, 	\nDatabricks	, Gitlab, GitHub 	etc	. 	\n \nEducation	: 	\nJP Institute of 	Engineering and Technology	 	\nBachelor of Science and Technology, 	 	\nComputer Science &amp; Engineering	 	\n2014 	&ndash; 2018',2,2,1,'Recruiter','SL','N',14,1,'2024-07-25 15:05:30','2024-07-22 15:05:30',0,NULL,NULL,'2024-07-21 18:25:06',0),(437,103,76,NULL,NULL,'76_103_29131285669d5282a7195_Resume_Ankit_w2_12th-June_(1).pdf','Resume_Ankit_w2_12th-June (1).pdf','Ankit \nChand	 	\n09438669636\nchand07ankit@gmail.com	 	\nhttps://www.linkedin.com/in/ankitchand007/	 	\nData Scientist with 	3 years	 of hands	-on experience in 	developing	 and 	deploying	 data	-driven solutions to solve complex business 	\nproblems. 	 	\n* Good experience in 	Python	 for 	data analysis, machine learning	, and 	automation	. Skilled in using 	libraries	 such as 	Pandas, 	\nNumPy, Scikit	-Learn, TensorFlow	, and 	PyTorch	. 	\n* Proficient in 	developing 	various types of 	models	, including 	regression, classification, clustering	, and 	deep learning models	.  	\n* Experienced in 	model selection	, hyperparameter tuning	, and 	performance evaluation	. 	\n* Expert in 	exploratory data analysis	 (EDA), feature engineering	, and 	data visualization	 techniques.	 	\n* Excellent 	communication skills	 for collaborating with cross	-functional teams and presenting results to stakeholders.	 	\nProfessional 	Experience	 	\n09	/202	2 &ndash; present	 	\nRetisio India Pvt Ltd	, 	\nBengaluru, IN 	 	\n \n 	\nData Scientist	 	\nClient: 	AllenBrothers	 - Designed and implemented a 	Recommendation Engine	 (RxE	) to	 recommend 	\nmost relevant items to particular user based on their interests in E	-commerce	 	\n* Analysed	 historical sales data, customer reviews, and ratings to identify trends and opportunities for 	\nimprovement.	 	\n* Incorporated 	features	 such as product ID, category, brand, price, description, and technical 	\nspecifications to enrich product listings.	 	\n* Implemented a 	recommendation engine	 leveraging anonymized customer purchase history and 	\nbrowsing behaviour. 	 	\n* Utilized data on product returns and 	exchanges to identify potential quality issues and optimize 	\ninventory management.	 	\n \nRETISIO Intelligent Search 	- To build a product that will identify and provide relevant search results	 	\n* Developed to 	automate	 the updating catalogue specific keywords and entity of ner dictionary.	 	\n* Developed 	search intent identification	 for price, product, type, and category.	 	\n* Integrated	 script for 	multitency support	 (as site1 and site2)	 	\n \nLearning to Rank for E	-Commerce Search 	- Developed Python script for managing features (likely 	 	\nsearch queries) in an Elasticsearch index.	 	\n- Load features as a feature set into an 	Elasticsearch index	. 	\n* Forged the 	Data preparation	 and 	preprocessing	 for model training	 	\n* Developed 	&amp; trained	 the model with 	fine	-tuned hyper parameters	 	\n \nData Scientist	 	\nClient: BrandsMart	 USA 	- Designed and implemented a Search Intelligence Tool (SIT) to improve search 	 	\nrelevancy for an e	-commerce website	 	\n* SIT employs 	machine learning models	, specifically a 	Conditional Random Field (CRF) model	, to 	 	\nidentify entities (brands, categories, subcategories) within a user&#039;s search query.	 	\n* The model undergoes two critical phases: 	training 	and 	inference	. During training, the model learns 	 	\nfrom 	labeled data	 to improve its prediction accuracy. The inference phase utilizes the trained model to 	 	\nanalyze 	user queries and assign relevant labels	. 	\nEnhancement	: Identified 	missing data points	 in NER dictionary and unigram.txt. Developed a process 	\nto update missing display names, brand names, model codes, and SKU codes.	 	\n* Maintained 	data quality	 by filtering noisy data (e.g., &quot;808&quot; in brand names). Addressed model code 	 	\nauto	-correction issues by manually editing unigram and skipping for symspell files.	 	\n* Implemented a recurring update process (every 2	-3 months) to ensure data freshness.	 	\n* Streamlined data comparison	 by sorting model codes based on length and filtering numeric/float 	 	\ndata.	 	\n* Optimized 	SymSpell usage by balancing number of edits and frequency for better auto	-correction.	 	\n \n 	\nProfile Summary	 	\n 	\n  \n  	\n 	\n09/2021 	&ndash; 09/2022	 	\nAIE Software	 India Pvt 	\nLtd	, Bengaluru, IN\n\nSkills       	 	\nLanguage 	- Python, SQL, PySpark	 	\nFrameworks &amp; Databases 	- Flask, PostgreSQL, MySQL	 	\nDevelopment Tools and Services 	- Gitlab, Jupyter Notebook, PyCharm, Visual studio, Postman	 	\nCloud Platforms 	- GCP [ Big Query, Dataproc, Google App Engine, Google Cloud Storage, Cloud Composer 	\n(Airflow) and Logging]	 	\nEducation	 	\nPost Graduate Diploma in Data Science	 	\n03/2020 	- 04/2021 &bull; IIIT Bangalore &amp; upGrad	 	\nI have learned Statistics, Machine Learning, and Deep Learning, and Many other aspects of Data Science which \nare required to grow as an expert in this field. Secured 2.96 / 4.00	 	\n \nB. Tech Mechanical Engineering	 	\n08/2013	-07/2017 &bull; Lovely Professional University	 	\nJalandhar, Punjab	 	\nI have done my Majors in Mechanics such as Dynamics of Machines and Mechanisms. I have done my Minors \nin Reading of Contemporary Fiction which included Social, Love, and Life Moral Values. Secured 61.83%.	 	\nI Participated in International Go	-kart Championship (IGC) in LPU and secured Forth Place. LPU always gave a 	\ncompetitive spirit of participating in events.	 	\nLanguages	 	 	English,	 Hindi	 and 	Odia',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 18:25:06',0),(438,103,76,NULL,NULL,'76_103_856725183669d5282a73ca_Rithiks_Resume.pdf','Rithik\'s Resume.pdf','Rithik Mathur	 	\nConatct No: 9462437371, 	8740880055	 Email Id	: rithikmathur97@gmail.com	 	\nGithub Id: 	https://github.com/Rithikmathur	 	\nExperience	 	\n \nPiSquare	 	 	 	 	 	 	 	 	Bengaluru, Karnataka, India	 	\nMachine Learning 	Engineer	 	 	 	 	 	 	01/2022	-presen	t 	\n 	HR Management System	 	\nI have designed and created a 	system	 which	 uses machine learni	ng algorithms for the	 probability	 	\nprediction	 of  a  person  to  stay  in  a  company,  predicting  is  sur	vival  probabilities	 and  graph	 based 	\non their fe	atures such as location, region, achievements, hikes, etc. for different companies.	 	\n 	Research	 	\nI am  also  re	searching  some  Gen  AI  models  for  different  application  purposes	, models  s	uch  as 	\nGPT	-4, and as latest as 	Llama 2	. 	\nAvinex Softwares Pvt. L	td.                                                              	                       	Jaipur, India	 	\nData Scientist	                                                                           	                              	03/2020	-05/2021                    	 	\n 	Ridge Regression	 	\nI  have  done 	Outlier  Detection  using  Statistical  Methods 	and 	PYOD	 Library	 and  sklearn  Library 	\nand after cleanup imputation is done using libraries such as sklearn, fancy impute, auto impute in \nthe dataset and then perform a ridge regressio	n on this model.	 	\n 	Document Extraction	 	\nI have extracted m	ultiple documents for different formats such as PDFs, Eml, Xlsx, Xlsm, Docx, 	\netc. Extracted from the 500,000 emails.	 	\n 	Content Extraction	 	\nIt  was  We	b  Scrapping  process  to  execute	 NLP(Natural  Language  Proc	essing)  Pipeline.  I  have 	\ndone  content  extraction  from  the  Wikipedia  using  Wikipedia  Library  and  clean  it  up  using 	nltk 	\nand regex and then create	 solr core for it.	 	\nAxis India Machine Learning Lab	 	Jaipur, India	 	\nMachine Learning Intern	 	05/2019	-01/2020	 	\nAIML is a leading institution for artificial intelligence &amp; machine learning mentor 	by Mr Jaskirat Singh	 	\n 	Learning  and  working  with  Probability  and  Statistics,  Linear  Algebra,  Convex  Optimization, \nMachine  Learning  and  Deep  Learning  algorithms.  Developed  sev	eral  algorithms  from  scratch 	\nwith deep understanding of the math behind.	 	\nEducation	 	  \nJECRC University 	(Bachelor of Technology in Computer Science)	                      	           	CGPA 8.10/10	 	\n \nSkills	 	\n \nPROGRAMMING LANGUAGES: 	Python	 	\nSOFTWARE: 	Jupyter	 Notebook, Microsoft Office	, Visual	 Studio	 Code	, Pycharm	 	\nSKILLS:	- Machine 	Learning	(Supervised  Learning,  Unsupervised  Learning)	,  Deep  Learning,  NLP, 	\nTransformers, Attention Mechanism, BERT, LLMs, Docker, Git, T	ensorflow, Keras, Pytorch, Kedro, DVC	, 	\nLlama Index, RAG	 	\n \nProjects	 	  	 	\n 	Blog Generation Using LLM Llama 2	 	\n 	Question Answering System	 	\n 	Multivariate	 Na&iuml;ve 	Bayes	 Classifier	 on	 Devanagari	 Dataset	 from	 Scratch	 	\n 	For more, kindly visit my github id.',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 18:25:06',0),(439,103,76,NULL,NULL,'76_103_1108534484669d5282a75b0_Roshan_AI3.pdf','Roshan_AI3.pdf','Rosh an	\nAI/M L E ngin eer	\nro sh an .y a d av2 8 12 97@ gm ail. c o m	 	96 430 37 771	 	G uru gra m , I n dia	 	\nh ttp s:/ /w ww.li n ked in .c o m /in /r o sh an -y a d av/	 	\nP R O FIL E\nAI/M L E ngin eer w it h  3  y e a rs  o f e x p erie n ce  i n  i n te rp re tin g a n d a n aly z in g d ata  t o  f u el c o rp ora te  g ro w th .	 	\nP ro fic ie n t i n  b u ild in g r o b u st m ach in e l e a rn in g m od els  t h at p ro vid e v a lu ab le  i n sig h ts  e n ab li n g d ata -d riv e n	 	\nd ecis io n -m ak in g.\nP R O FE SSIO N AL E X PE R IE N CE\nAI/M L 	E ngin eer	\niO PE X  	Tech n olo g ie s 	P riv a te  	L im it e d	\n&bull;P erfo rm ed  D ata  V is u ali z a tio n , e x p lo ra to ry  d ata  a n aly sis  a n d s ta tis tic a l a n aly sis  t o	 	\ng en era te  i n sig h ts .	\n0 9/2 0 21 &ndash;  p re se n t\nG uru gra m , I n dia	\n&bull;P erfo rm ed  T ra in in g, H yp erp ara m ete r t u n in g, a n d M od el S ele ctio n .\n&bull;D eve lo p ed  a  c o m pre h en siv e  s e n tim en t a n aly sis  m od el u sin g T ra n sfo rm ers  f o r a	 	\nc li e n t b y a n aly z in g c u sto m er a n d a g en t c h at t r a n sc rip tio n s.\n&bull;W ork ed  a n d T ra in ed  C on vo lu tio n al N eu ra l N etw ork s(C N N) u sin g T en so rF lo w  a n d	 	\nN atu ra l L an gu ag e P ro ce ssin g ( N LP ) m od els .\n&bull;U pli f te d  a  M ach in e L ea rn in g i n te n t c la ssif ic a tio n  m od el a cc u ra cy  b y 1 0 % .\n&bull;Fin e-tu n ed  L L M (L la m a2 ) A I m od el f o r b u sin ess u se  c a se .\n&bull;C olla b ora te d  w it h  5 +  d ata  e n gin eers  t o  i m pro ve  t h e m od el p re d ic ta b ili t y .	\nJu n io r 	D eve lo p er 	(I n te rn )	\nVse r v  	In fo sy ste m s 	P riv a te  	L im it e d	\n&bull;D eve lo p, t e st, a n d m ain ta in  s c a la b le  P yth on  a p pli c a tio n s.	\n0 6/2 0 18  &ndash;  0 7/2 0 18	\nD elh i,	 I n dia	\n&bull;U tili z e P yth on  l i b ra rie s f o r e ffic ie n t a n d e ffe ctiv e  c o d e i m ple m en ta tio n .\n&bull;D esig n  a n d i m ple m en t R EST fu l A PIs  f o r s e a m le ss  c o m mun ic a tio n  b etw een	 	\nco m pon en ts .	\nS K IL L S\nEnd t o  E nd d ep lo ym en t |  D eep  L ea rn in g |  A uto m atio n  |  D ock er |  A PI ( F la sk , F astA PI) |  S Q L |  G IT  |  L O RA |\nM ach in e L ea rn in g A lg orit h m s(L in ea r R eg re ssio n ,L og is tic  R eg re ssio n ,D ecis io n  T re e,K -M ea n s C lu ste rin g) |\nP yth on (S cik it - le a rn , P an das, N um py) |  T en so rF lo w  |  L arg e L an gu ag e M od el( L L M ) |  L an gch ain  |  K afk a |\nC N N |  R N N |  T ra n sfo rm ers  |  N LP  |  N LT K  |  P yto rc h  |  W ord 2V ec |  B ER T |  V ecto r D ata b ase  |\nP R O JE C TS\nData In sig h ts  	- I n te n t 	a n d 	E ntit y  	R eco gn it io n  	M od el 	u sin g 	sp aC y	\nO bje ctiv e	: D eve lo p ed  a  n atu ra l l a n gu ag e p ro ce ssin g m od el u sin g s p aC y t o  r e co g n iz e i n te n t a n d e n tit ie s i n	 	\nt e x tu al d ata .	\n&bull;D ata  C olle ctio n  a n d A nnota tio n	: R ece iv e d  u n la b ele d  d ata  f r o m  t h e c li e n t. L ab ele d  t h e d ata  w it h  t h e h elp  o f	 	\nn er- a n nota to r.\n&bull;D ata  P re p ro ce ssin g	: C on du cte d  d ata  p re p ro ce ssin g l i k e r e m ovin g t h e s to p -w ord s, L em matiz a tio n  e tc .	\n&bull;M od el I n te g ra tio n	: T ra in ed  m od el( e n _co re _w eb _sm ) o n  o u r d ata  u sin g s p aC y a rc h it e ctu re  b y l o ad in g a  p re -	\ntr a in ed  E ngli s h  l a n gu ag e m od el p ro vid ed  b y s p aC y.\n&bull;T ra in in g a n d E va lu atio n	: T ra in ed  t h e m od el,  m on it o re d  t r a in in g p ro g re ss a n d o p tim iz ed  h yp erp ara m ete rs	 	\nto  a ch ie ve  o p tim al p erfo rm an ce .\n&bull;R esu lt s	: T he t r a in ed  m od el a cc u ra te ly  r e co g n iz ed  i n te n t a n d e n tit ie s i n  t e st d ata , w it h  a n  o ve ra ll a cc u ra cy  o f	 	\nove r 9 0 % .	\nEnvir o n m en t	 :  S pacy, P an das, F astA PI, D ock er, G IT	\n                                                                                                                                                               	r o sh an .y a d av2 8 12 97@ gm ail. c o m\n\nReg alA I 	- L arg e 	L an gu ag e 	M od el( L L M )	\n F in e-T u n ed  a n d i n te g ra te d  a  A rtif ic ia l I n te lli g en ce (A I) L arg e L an gu ag e M od el( L la m a-2 ) t h at c a n	 	\nh elp  i n  m ult ip le  b u sin ess r e q u ir e m en ts  a n d d eve lo p  a  q u estio n -a n sw erin g m od el.	\n&bull;D ata  P re p ara tio n :	 P re p are d  a  q u estio n -a n sw erin g d ata  a n d t h en  i n te g ra te d  i t  i n  t h e f o rm at o f p ro m pt	 	\nw hic h  L la m a-2  a cce p ts .\n&bull;M od el F in e-T un in g	: T ra in ed  t h e m od el u sin g S u perv is e d  F in e T u n in g(S FT ) w it h  t h e h elp  o f Q LoR A.	\n&bull;M od el E va lu atio n	: R evie w ed  t h e t r a in in g r e su lt s  i n  t h e T en so rb oard  a n d b y c h eck in g t h e o u tp u t b y	 	\np ro vid in g s a m ple  p ro m pts .	\nE nvir o n m en t	 :  L la m a2 , T ra n sfo rm ers, P eft, B it s a n dbyte s, D ock er, G IT	\nC hatS en tin el 	-  S en tim en t 	A naly sis  	fo r 	C usto m er 	C hat 	T ra n sc rip ts	\nO bje ctiv e	: D eve lo p ed  a  c o m pre h en siv e  s e n tim en t p re d ic tio n  m od el f o r a  p ro m in en t c li e n t b y a n aly z in g	 	\ncu sto m er a n d a g en t c h at t r a n sc rip tio n s. E m plo ye d  a d va n ce d  N atu ra l L an gu ag e P ro ce ssin g t e ch n iq u es t o	 	\na cc u ra te ly  c la ssif y  i n te ra ctio n s i n to  p osit iv e, n eu tr a l,  a n d n eg ativ e  s e n tim en ts .	\n&bull;D ata  G ath erin g	: E sta b li s h ed  a  r o b u st c o n nectio n  t o  t h e c li e n t&#039;s  M yS Q L d ata b ase , e x tr a ctin g c h at t r a n sc rip ts	 	\nfo r s e n tim en t a n aly sis .\n&bull;D ata  C le a n in g	: P erfo rm ed  T ex t C le a n in g, L ow erc a sin g, T o ken iz a tio n , S to p w ord  R em ova l,  S te m min g a n d	 	\nLem matiz a tio n , H an dli n g A bbre via tio n s, R em ovin g N on -T ex tu al c o n te n t, H an dli n g N ois e , E nco d in g	 	\nE m otic o n s a n d S ym bols .\n&bull;S en tim en t A naly sis	: I m ple m en te d  C hatS en tin el u sin g p re -tr a in ed  N LP  m od els (e .g ., R ob erta ) f o r a cc u ra te	 	\ns e n tim en t c la ssif ic a tio n .I f t h e i n pu ts  e x ce ed s 5 12  t o ken s, c re a te d  b atc h es o f 5 12  t o ken s a n d t h en  c o m bin ed	 	\nth e s c o re s t o  g et s e n tim en t.\n&bull;A PI D eve lo p m en t	: D esig n ed  a n d d ep lo ye d  C hatS en tin el,  a  h ig h -p erfo rm an ce  R EST fu l A PI, u sin g F astA PI.	\n&bull;C on ta in eriz a tio n	: D ock eriz ed  C hatS en tin el f o r p orta b le  d ep lo ym en t a n d s c a la b ili t y .	\nE nvir o n m en t	 :  P yth on , T ra n sfo rm ers (H uggin g f a ce ), F astA PI, D ock er, G IT	\nS u pportf ir st 	-  R etr ie va l- A ugm en te d  	G en era tio n  	(R AG ) 	f o r 	c u sto m er 	su pport	\nO bje ctiv e	 -  D eve lo p  a  c u sto m er s e rv ic e  c h atb ot t h at l e ve ra g es R etr ie va l- A ugm en te d  G en era tio n  ( R AG ) t o	 	\np ro vid e m ore  a cc u ra te , i n fo rm ativ e, a n d u p-to -d ate  a n sw ers  t o  c u sto m er i n qu ir ie s.	\n&bull;D ata  C olle ctio n  a n d P re p ro ce ssin g	 -  G ath er c u sto m er s e rv ic e  d ata  f r o m  v a rio u s s o u rc e s, s u ch  a s F A Q s,	 	\np ro d u ct m an uals , a n d p ast s u pport t ic k ets . P re p ro ce ss t h e d ata  b y c le a n in g a n d s tr u ctu rin g.\n&bull;L arg e L an gu ag e M od el ( L L M ) I n te g ra tio n	 -  C hoose  a n d i n te g ra te  a  p re -tr a in ed  L L M (L la m a2 ) i n to  t h e	 	\nsy ste m  u sin g b it s a n dbyte s t o  r e d u ce  m em ory  c o n su m ptio n  f o r i n fe re n ce .\n&bull;V ecto r D ata b ase  s e tu p 	- C hoose  a n d s e tu p a n  o p en -s o u rc e  v e cto r d ata b ase (c h ro m ad b) t o  s to re  a ll t h e d ata	 	\ne m bed din gs. E m bed ded  t h e d ata  u sin g S en te n ce -T ra n sfo rm er(a ll- M in iL M -L 12 -v 2 )\n&bull;R etr ie va l C om pon en t D eve lo p m en t	 -  D eve lo p ed  a  r e tr ie va l s y ste m  t h at c a n  e ffic ie n tly  s e a rc h  t h e	 	\npre p ro ce sse d  c u sto m er s e rv ic e  d ata  b ase d  o n  u se r q u erie s u sin g s e n te n ce  e m bed din g.\n&bull;R AG  I n te g ra tio n  a n d R esp on se  G en era tio n	 -  D eve lo p ed  a  p ip eli n e f o r t h e L L M  t o  i n te ra ct w it h  t h e r e tr ie va l	 	\nsy ste m  u sin g L an gC hain . T he L L M  p ro ce sse s t h e r e tr ie ve d  i n fo rm atio n  a lo n gsid e i t s  o w n k n ow le d ge t o	 	\ng en era te  a  c o m pre h en siv e  a n d i n fo rm ativ e  r e sp on se  t o  t h e u se r&#039;s  i n qu ir y.	\nE nvir o n m en t 	: L la m a2 , C hro m ad b, L an gC hain , T ra n sfo rm ers	\nE D UCATIO N\nBach elo r 	o f 	T ech n olo gy 	( B .T ech  	)	\nD ro n ach ary a  	C olle g e 	o f 	E ngin eerin g',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 18:25:06',0),(440,103,76,NULL,NULL,'76_103_178953194669d5282a7758_Shilpa_Shaw_Resume.pdf','Shilpa Shaw Resume.pdf','Shilpa   Shaw  \n \nData   Scientist   |   Data Analy st   |  Machine   Learning Engineer   | AI   Enthusiast  \n  +91 	- 9903369681\n 	 shilpashaw9681@gmail.com  	 LinkedIn  	 GitHub  	 \nPortfolio	 \n \n     SKILLS   AND   TOOLS  \n&bull;  \nProgramming &amp; Development:  Python, Scikit - Learn, TensorFlow, Keras, PyTorch, Transformers, Beautiful Soup, Librosa, EDA, ETL, \nPySpark, Matplotlib, Plotly, Open - CV, SQL, Flask, Docker, GitHub Action, Git    \n&bull;  \nData Science &amp; Al:   Deep Learning, Data Analysis, Pre dictive Modeling, Artificial Intelligence, Statistical Modeling, Data Visualization, Time \nSeries Analysis, Cloud Machine Learning (AWS), NLP, Prompt Engineering, Generative Al, LangChain, LLama, LLMs.  \n&bull;  \nData Analysis &amp; Visualization:   MS SQL (SSMS), MS Excel   ( Pivot tables   &amp;  Pivot Charts ) , Power BI   (DAX) , IBM SPSS  \n&bull;  \nDeveloper Tools:   Github, VScode, Jupyter, Colab, pip, Docker, HuggingFace, Jenkins, OpenAI GPT - 3.5  \n&bull;  \nSoft Skills:   Analytical skills, Effective Communication, Collaboration, Team Building, data - driven de cision - making, problem - solving, Curiosity    \nWORK   EXPERIENCE      \nData   Scientist / Machine learning  intern   |   Vlippr   Jan   &#039;24  \n-   Present  \n&bull;   Audio Database Management &amp; Analysis : Proficient in managing and analyzing audio databases with GCP Console and YouTube Data  API \nv3, integrated with SSMS for comprehensive data handling.  \n&bull;   NLP &amp; Text - to - Speech Expertise : Advanced experience in deploying large language models like Whisper, X - TTS, MetaVoice, and Wav2Vec \nfor text - to - speech tasks, achieving up to 90% accuracy through  meticulous fine - tuning.  \n&bull;   Speech Processing &amp; AI Dubbing : Adept in speech - to - text processing using Wav2Letter, with a focus on feature extraction for style and tone, \nachieving 95% accuracy. Skilled in enhancing AI - powered multilingual dubbing, significantly  improving production workflows and reducing \ntime - to - market.      \nData   Scientist   Intern   |   Intellipaat   Apr   &#039;23  \n-   Dec   &#039;23  \n&bull;   Data Science Collaboration : Engaged in data operations, including collection, cleaning, and preprocessing, leveraging Python, SQL, MS \nExcel, and Power BI to support team objectives.  \n&bull;   Dashboard Migration &amp; Enhancement : Executed the migration and feature enhancement of merchandising dashboards, focusing on data \nintegration and improvement using DAX for optimal performance.      \nResearcher  |   Writerbay   Aug   &#039;21  \n-   Dec   &#039;23  \n&bull;   Recognizes the importance of research data management as a fundamental aspect of resource management across various domains, \ncontributing to the overall efficiency and success of research projects.  \n&bull;   Created extensive projects  and assignments centered around statistical concepts, harnessing the power of Microsoft tools and IBM SPSS.      \nResearch Data management  |   Royal   Research   Jan   &#039;1 8  \n-   Sep   &#039;21  \n&bull;   Expert in crafting data organization and storage strategies for quantitative and  qualitative research   throughout the project&rsquo;s duration.   \n&bull;   Adept at applying statistical inference with SPSS, NVivo, and RStudio for comprehensive primary and secondary analysis.  \n    \nPROJECTS      \nBoston House Pricing Prediction:  End to End Machine Learning Project Implementation with Dockers, GitHub Actions and Deployment.   \nFinetune LLM models:  Fine - tuned llama2 model with custom dataset using PEFT, LORA, QLORA via quantization and calibration to get 90% accuracy.    \nSpearheaded a  comprehensive Cricket Data Analytics Project : Employed Web Scraping techniques to extract T20 World Cup data from ESPN Cricinfo, \nfollowed by data cleansing and analysis using Python and Pandas, culminating in insightful visualizations with Power BI.  \nMusic  Store Data base   Analysis : Facilitated   the   analysis   using SQL to  help the store understand its business growth  strategies   by 50% at EOY.    \nCredit Card Financial Dashboard : To develop a comprehensive credit card weekly dashboard that provides real - time  insights into key performance \nmetrics and trends, enabling stakeholders to monitor and analyze credit card operations effectively.  \nWalmart   Sales   Forecasting :  Forecasted   sales   for   the   next   12   weeks   using   predictive   modeling   techniques using ARIMA, SARIMA  time   series   model.  \nRVC (Retrieval - based Voice Conversion) model - based LLM project : Created a speech - to - speech translation of data using the RVC v2 model for voice \ncloning and transformed AI voice   obtaining 95% accuracy in audio output   with cloned voice .  \nAT S Friendly resume:   Leveraged Google Gemini Pro Vision LIM Model to enhance resume analysis, aligning with job descriptions and providing \nactionable improvement suggestions, resulting in a 25% increase in interview callbacks for job seekers.  \nEnd - to - End Adva nced RAG Project : Llama2 and Mistral integrated with Groq&#039;s high - performance inferencing engine to enhance real - time AI response.    \n \nEDUCATION   AND   CERTIFICATIONS  \n&bull;   PGP   (Post Graduation Program)   in   Data   Science   and   Machine   Learning   ( SQL   &amp;   Python )   |   Intellipaat   |   Dec   2023  \n&bull;   Machine   Learning   with   Python:   MITxMicroMasters/IDSS   |   MIT   -   Massachusetts   Institute   of   Technology   |   Dec   202 3  \n&bull;   B.Tech   in   Electronics   and   Communications   Engineering   (ECE)   |   MAKAUT   (WBUT)   |   Aug   2013   -   Jul   2017  \n&bull;   Generative AI with Large  Language Models | DeepLearning.AI, AWS | Coursera | March 2024  \n&bull;   Microsoft   Azure   Data   Scientist   Associate   (DP - 100)   Professional   |   Coursera   and   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P ro fic ie n t i n  d ata b ase  m an ag em en t a n d s k ille d  i n  v ario u s\npro gra m min g la n gu ag es,  in clu din g P yth on ,  M yS Q L,  a n d B ase  S A S.  E xp erie n ce d  in  m ach in e le a rn in g,  d eep  le a rn in g,\nd ata  a n aly sis ,  q u alit y  a ssu ra n ce ,  a n d p ro d u cin g e n d-u se r d ocu m en ta tio n .  P artic u la rly  a d ep t a t l e v e ra g in g P ow er B I f o r\nd ata   vis u aliz a tio n   an d  cra ft in g  co m pellin g  narra tiv e s  fo r  m ed ic a l  co n fe re n ce s.  Su cce ssfu lly   dev e lo p ed   m ult ip le\nm ach in e  le a rn in g  an d  deep   le a rn in g  alg o rit h m s  fo r  re g re ssio n   an d  cla ssif ic a tio n   ta sk s,  w it h   a  fo cu s  on   te x t\ncla ssif ic a tio n ,  m ach in e t r a n sla tio n ,  a n d im ag e c la ssif ic a tio n  in  o n co lo gy d is e a se s,  a s d em on str a te d  in  p ro je cts  f o r o u r\npro d u ct F erm a.A I.	\nP R O FE SSIO N AL E X PER IE N CE	\nExce lR  	EdT ech  	Priv a te  	L im it e d	\nD ata  	A naly st\n&bull;L ed  t h e d ev e lo p m en t o f a  p re d ic tiv e  m od el u sin g m ach in e l e a rn in g t o  f o re ca st	 	\nc u sto m er c h urn ,  r e su lt in g i n  a  1 5%  i m pro ve m en t i n  r e te n tio n  r a te s.	\nM ar&nbsp;2 023 &ndash;  p re se n t	\nB an galo re ,	 I n dia	\n&bull;A uto m ate d  d ata  e x tr a ctio n  t a sk s b y s tr e a m lin in g p ro ce sse s w it h  P yth on  a n d S Q L,	 	\nach ie v in g a  w eek ly  t im e s a v in gs o f 1 5 h ou rs .\n&bull;U tiliz e d  P ow er B I t o  v is u aliz e  c o m ple x  d ata ,  f a cilit a tin g e a sie r i n te rp re ta tio n  a n d	 	\nin fo rm ed  d ecis io n -m ak in g b y t h e m an ag em en t.	\nO rb it h ru st 	C on su lt in g 	P riv a te  	L im it e d	\nA sso cia te  	D ata  	A naly st	\n&bull;C on du cte d  m etic u lo u s d ata  e x tr a ctio n  a n d m in in g o f c lin ic a l d ata  f r o m	 	\nC lin ic a lT ria ls .g o v,  e n su rin g c o m pre h en siv e  d ata  c o ve ra g e.	\nJa n &nbsp;2 022 &ndash;  F eb &nbsp;2 023	\nC hen nai,	 I n dia	\n&bull;E xecu te d  a  t h oro u gh  q u alit y  c h eck  o n  t h e a n aly ze d  d ata  b y c ra ft in g a  c o h ere n t	 	\nsto ry lin e a n d o rg an iz in g i n sig h ts  t h ro u gh  e ffe ctiv e  b u ck etin g.\n&bull;P re p are d  d eta ile d  d ocu m en ta tio n  o u tlin in g t h e s to ry lin e f o r m ed ic a l c o n fe re n ce s,	 	\nfa cilit a tin g t h e d is se m in atio n  o f k ey  f in din gs a n d i n sig h ts  t o  s ta keh old ers .\n&bull;U tiliz e d  R N N a n d C N N m od els  t o  p erfo rm  t e x t c la ssif ic a tio n ,  i m ag e c la ssif ic a tio n  a n d	 	\nm ach in e t r a n sla tio n  o n  o n co lo gy r e la te d  i m ag es a n d s ta te m en ts ,  e n han cin g t h e	 	\nca p ab ilit ie s o f o u r p ro d u ct F erm a.A I i n  u n ders ta n din g a n d p ro ce ssin g m ed ic a l d ata .	\nB io n iv id  	T ech n olo gy	\nD ata  	A naly st 	I n te rn	\nP erfo rm ed  d ata  a n aly sis  o n  N GS u sin g M S e x ce l,  f a stq c,  c u ta d ap t,  f a stp ,  b ow tie ,	 	\nsa m to ols ,  b cft o ols , V EP	\nM ay&nbsp;2 021 &ndash;  N ov&nbsp;2 021	\nB an galo re ,	 I n dia	\nP R O JE C T S	\nAI/M L 	P ro je cts	\n&bull;D ev e lo p ed  m ult ip le  c h atb ots  u tiliz in g n eu ra l n etw ork s a n d N LP  l ib ra rie s s u ch  a s N LT K ,  G en sim ,  a n d o th ers ,	 	\nsh ow ca sin g p ro fic ie n cy  i n  A I- d riv e n  c o n ve rs a tio n al s y ste m s.\n&bull;C re a te d  i m ag e d ete ctio n  a lg o rit h m  u sin g O pen cv.\n&bull;C re a te d  t e x t c la ssif ic a tio n  u sin g p re  t r a in ed  B ER T  m od el.\n&bull;I m ple m en te d  i m ag e c la ssif ic a tio n  u sin g t r a n sfe r l e a rn in g o n  t h e R esN et m od u le ,  s h ow ca sin g a n  i n novativ e	 	\na p pro ach  t o  l e v e ra g in g p re -tr a in ed  m od els  f o r i m pro ve d  p erfo rm an ce .\n&bull;C re a te d  a  l o gis tic  r e g re ssio n  m od el t o  d ia b etic s d is e a se  w it h  8 7%  a cc u ra cy.\n&bull;C re a te d  a  d ecis io n  t r e e a n d r a n dom  f o re st m od el f o r l e n din g c lu b l o an  p re d ic tio n .	\nS K IL LS	\nPyth on (P an das,  N um py,S ea b o rn ,	 	\nSkle a rn ,T en so rflo w	 	\nN LT K ,P yto rc h ,O pen C V )\nM ach in e  L ea rn in g(D ecis io n  T re e,	 	\nL o gis tic  R eg re ssio n ,  S V M ,  K	 	\nm ea n s,  C lu ste rin g,	 	\nR eco m men datio n  S yste m  e tc )	\nO OPS\nSQ L	\nCN N,  L ST M ,  B ER T,  C BO W ,	 	\nSK IP G RA M ,  Y O LO ,  G AN ,  F o u rie r	 	\nT ra n sfo rm er,  G LO VE\nPow er B I,  B ase  S A S',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 18:25:06',0),(443,103,76,NULL,NULL,'76_103_1962274146669d5282a7c59_Varun_Achar_M_N_GenAI.pdf','Varun_Achar_M_N_GenAI.pdf','VARUN ACHAR M N	 	\nvarunachar91@gmail.com	 | https://www.linkedin.com/in/varun	-achar	-m-n/ | https://github.com/hayavar	 | Ph: +918105918846	 	\n 	\nProfile	 Summary	 	\nHighly motivated and results	-driven 	professional	 with 	around 4	 years of experience in 	Robotic	 Process 	Automation (RPA) and 	\nGenerative AI, seeking to leverage expertise in automating business processes and building AI solutions to contribute to 	the	 company&#039;s 	\ngrowth and efficiency. Proven ability to identify, analyze, and automate tasks, collaborate with stakeholders, and deliver so	lutions that 	\nimprove productivity and reduce costs. Strong technical skills in RPA tools (Automation Anywhere, UiPath, K	ofax Kapow), 	\nGenerative AI platforms (LangChain), and Python. Eager to learn and contribute to innovative projec	ts in a fast	-paced environment	. 	\nProfessional Experience	  	\nSenior 	Associate Consultant (April 202	2 &ndash; Present)	 Infosys Ltd, Mysuru	, Karnataka.	 	\n&bull; 	Led the development of a hyper	-automation solution combining Generative AI, RPA (UiPath), and Python to streamline the 	\nprocesses in the BFSI, Manufacturing, and Healthcare industry.	 	\n&bull; 	Facilitated weekly conference calls with stakeholders in North America, APAC, and EMEA regions, providing operational \ninsights and fostering strong partnerships	. 	\n&bull; 	Developed state	-of-the	-art generative models using LangChain and Python to automate text generation tasks, resulting in a 	\n30% increase in content creation efficiency for clients in the BFSI sector	. 	\n&bull; 	Designed and implemented AI	-powered chatbots for customer service applications, enhancing user experience and reducing 	\nresponse times by 40%	. 	\n&bull; 	Fine	-tuned pre	-trained models to specific client datasets, significantly improving the relevance and accuracy of AI	-generated 	\ncontent and solutions.	 	\n&bull; 	Collaborated with data scientists and machine learning engineers to create end	-to-end AI solutions, including data 	\npreprocessing, model training, and deployment.	 	\n&bull; 	Conducted thorough research and experiments on transformer models and GPT architectures to improve the accuracy and \nperformance of AI applications.	 	\n&bull; 	Utilized Generative AI for automated testing of software products, creating realistic test scenarios and datasets that enhanc	ed 	\nthe efficiency and coverage of testing processes	 using techniques like RAG, CRAG 	and so on.	 	\n&bull; 	Integrated AI solutions with existing IT infrastructure using RESTful APIs, facilitating seamless adoption and integration fo	r 	\nclients.	 	\n&bull; 	Conducted rigorous testing and validation of AI models, ensuring high reliability and compliance with industry standards.	 	\n&bull; 	Led workshops and training sessions for internal teams on the implementation and benefits of Generative AI, fostering a \nculture of innovation.	 	\n&bull; 	Developed RPA solutions using UiPath and Automation Anywhere to automate various business processes, leading to a \nreduction in manual effort and processing time.	 	\n&bull; 	Created Proof of Concept (POC), System Design Document (SDD), Process Design Document (PDD), and flowcharts for \nprocess requirements, forming the foundation for successful process automation.	 	\nAssistant System Engineer (August 2020 	&ndash; April 2022)	 Tata Consultancy Services, Bengaluru, Karnataka.	 	\n&bull; 	Created Proof of 	Concept (POC), System Design Document (SDD), Process Design Document (PDD), and flowcharts for 	\nprocess requirements, forming the foundation for successful process automation.	 	\n&bull; 	Developed RPA bots using Automation Anywhere to automate resource	-intensive tasks for infrastructure monitoring, device 	\nhealth checks, and backup reporting.	 	\n&bull; 	Migrated robots from Automation Anywhere v11.3 to A360 to comply with new development standards and optimize \nefficiency.	 	\n&bull; 	Partnered with stakeholders to gather and refine requirements, evaluate process suitability for automation, and design optima	l 	\nworkflows.	 	\n&bull; 	Managed user access provisioning, bot deployments, license management, and infrastructure setup &amp; maintenance for \nAutomation Anywhere as an RPA Administrator.	 	\n&bull; 	Resolved production issues as a Problem Manager by investigating incidents, identifying root causes, collaborating with \nstakeholders for resolution, preventing recurrence, and documenting solutions for future reference.\n\nEducation	 	\n&bull; 	08/2016 	- 09/2020 8.98 CGPA (CS&amp;E) 	- P.E.S College of Engineering, Mandya, Karnataka, India.	 	\n&bull; 	06/2014 	- 03/2016 12th (II PUC) 	&ndash; 95 Percentile 	- Vijaya Vittala Composite P.U College	. 	\n&bull; 	05/2014 10th (CBSE) 9.0 CGPA 	- Maharshi Public School	, Mysuru.	 	\nSkills Matrix	 	\n&bull; 	RPA Tools 	&ndash; UiPath 	(Intermediate), Automation Anywhere (Proficient), Kofax Kapow (Intermediate)	 	\n&bull; 	Languages 	- Python	, HTML(Beginner), CSS(Beginner)	 	\n&bull; 	Database 	&ndash; SQL (Beginner), MongoDB (Beginner)	 	\n&bull; 	Cloud 	&ndash; Azure, Azure OpenAI Services	 	\n&bull; 	Agile Tools 	- ServiceNow	, Azure DevOps Board, Confluence	 	\n&bull; 	Frameworks 	&ndash; Agile, Scrum	, Waterfall	, ITSM	 	\n&bull; 	Technology 	&ndash; Generative AI, APIs, Microservices	, Flask	 	\n&bull; 	Design Tools 	- Figma, GIMP, Canva	 	\n&bull; 	Leadership 	- Project Management, Communication &amp; Collaboration,	 Problem	-Solving &amp; Decision	-Making	 	\n&bull; 	Industry Experience 	&ndash; BFSI, Manufacturing and Healthcare	 	\nCertifications	 	\n&bull; 	AZ 900 	- Microsoft Azure Fundamentals	 	\n&bull; 	Certified Business Analyst 	&ndash; Automation Anywhere	 	\n&bull; 	UiPath Certified Professional Automation Business Analyst Professional	 	\n&bull; 	Diploma 	&ndash; UiPath RPA Developer Foundation	  	\n&bull; 	Coursera Managing Talent 	- University of Michigan	 	\nHobbies:	 Cooking, Music, Badminton, Travelling',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-21 18:25:06',0),(444,104,1,NULL,NULL,'1_104_166189052669e035d62775_Ajay_Chaudhari_-_HMI_Developer.pdf','Ajay Chaudhari - HMI Developer.pdf','/  	\nA JAY  C H AU DH ARI\nSoft w are  D eve lo p er	\narc h au d hari1 4 0 2@ gm ail. c o m	h ttp s:/ /w ww.lin ke d in .c o m /in /a ja y -\nc h au d hari- 0 15 6 ba16 9/	\nK an ag aw a, J a p an	\nS U M MARY\nExp erie n ce d  S oft w are  D eve lo p er w it h  o ve r 4 .3  y e ars  s p ecia liz in g  in  A nd ro id -\nbase d  A uto m otiv e  In fo ta in m en t s y ste m s 	 \n 	\nP ostg re S Q L,  a n d  O doo.  I  a m  e ag er t o  c o lla b ora te  o n a d va n cin g  c u ttin g -e d ge	 	\nte ch no lo g y a n d  d eliv e rin g  e xc e p tio nal  r e su lt s .\nE X PER IE N CE\nOnsit e  D eve lo p m en t E ngin eer\nTata  C on su lt a n cy S erv ic e s L td	\n0 5/2 0 24	&nbsp;	K an ag aw a,  J a p an	\nA ssig ned  F e atu re :  R ad io  	 	\nR ad io ,  P od ca st)\nO nsit e  S PO C f o r R ad io  f e atu re ,  C lie n t c o m mun ic a tio n,  M an ag e S O P /  Q LU	 	\n/ M ain lin e d eliv e rie s f o r N is sa n  V eh ic le s,  B ug  t r ia g in g  a n d  f ix in g\nW ork in g  w it h  c ro ss-fu n ctio nal  t e am s s u ch  a s X peri,   L G ,  G oog le ,  B osc h ,	 	\netc .\nT e ch S ta ck:  A nd ro id ,  K otlin ,  J a va ,  A O SP,  A ASP	\nS yste m s E ngin eer\nTata  C on su lt a n cy S erv ic e s L td	\n0 1/2 0 22 	&nbsp;	G an d hin ag ar,  In d ia	\nN is sa n  IV I  f e atu re s:  T e le m atic s 	 	\nC onnm an  \nF i,   a n d  H ots p ot).\nH M I  d eve lo p m en t t h at w ork s w it h  A O SP a n d  H AL p ro vid ed  b y L G  a n d	 	\nB osc h ,  f e atu rin g  1 0 0\nB uilt  p yth o n s c rip t t h at r e d uce d  S ST R g en era tio n t im e b y 8 0 %\nTe ch S ta ck:  A nd ro id ,  K otlin ,  J a va	\nJ r. A pplic atio n  E ngin eer\nTin yE R P P vt. L td	\n1 0 /2 0 20  	&nbsp;	A hm ed ab ad ,  In d ia	\nO doo:  E R P B usin ess S uit e  o f o p en -s o urc e  b usin ess a p ps c o ve rin g  C RM ,	 	\neC om merc e ,  A cco un tin g ,  In ve n to ry ,  P O S e tc\nD eve lo p m en t o f c u sto m  m od ule s a s p er c lie n ts &#039;  n eed s.  A ddre sse d  m ain ly	 	\ng en eric  &amp;  s p ecif ic  is su e s d urin g  d ata b ase  u p gra d es.  1  g en eric  f ix  c a n	 	\nre so lv e  r o ug hly  1 0  C lie n ts  d ata b ase  u p gra d e is su e s\nTe ch S ta ck:  P yth o n,  P ostg re sq l,   J S ,  U bun tu	\nP yth on /O doo D eve lo p er\nBro w se ln fo  P vt. L td	\n0 3 /2 0 20  	&nbsp;	A hm ed ab ad ,  In d ia	\nD eve lo p ed  C usto m  L ib ra ry  m an ag em en t O doo A pp,  P ro d uct Q ualit y	 	\nA ssu ra n ce  A pp u sin g  g en eric  m od ule s\nC ontr ib ute d  t o  e xis tin g  O doo A pps lik e  B atc h  P ay m en t d urin g  in vo ic in g\nTe ch S ta ck:  P yth o n,  J S ,  O doo	\nE D U CATIO N\nBach elo r o f E ngin eerin g\nSA L E n g in eerin g  A nd  T e ch nic al I n stit u te , G TU	\n04 /2 0 17  	&nbsp;	A hm ed ab ad ,  In d ia	\nGP A	\n8.2 7	10	\nC om pute r E ng in eerin g  M ajo r	\nS KIL LS\nTe ch S ta ck\nA nd ro id	J av a	K otlin	P yth on	\nO doo	P ostg re sq l	M atp lo tL ib	\nN um py	O pen C V	Pan d as	S eab orn	\nS kle arn	G it	\nT e ch nic al  E xp ert is e\nA uto m otiv e  I n fo ta in m en t f o r C CS\nER P S oft w are  	M ac h in e L e arn in g	\nP R O JEC TS\nAuto m atic  A tte n dan ce  S yste m	\nhttp s:/ /g it h ub .c o m /A jja rv is /A uto m atic _A tte n d an ce _S\ny ste m	\nF ace  R eco g nit io n-b ase d  A uto m atic  A tte n d an ce	 	\nS yste m  u sin g  C CTV  f o r r e al- tim e s tu d en t	 	\natte n d an ce .	\nTe ch S ta ck:  P yth o n,  T kin te r,  X lr d ,  X ls x w rit e r,	 	\nO pen C v	\nIn diM on ey : A n E xp en se  T ra cke r A pp	\nhttp s:/ /g it h ub .c o m /A jja rv is /In d iM oney	\nA nd ro id  E xp en se  T ra cke r A pp f o r e ffic ie n tly	 	\nm onit o rin g  o f e xp en se  a n d  in co m e w it h  le ss e ffo rts	\nT e ch S ta ck:  A nd ro id ,  S Q Lit e ,  G oog le  M L k it	\nA C H IE V EM EN TS	\nPala d in  o f t h e I V IT  C ham pio n s\nRece iv e d  b eca u se  o f h ig h p erfo rm an ce  o n	 	\ncrit ic a l  p hase  o f t h e p ro je ct\nD ig it a l M ile sto n e a t T C S\nCle are d  c o d in g  c h alle n g es in  P yth o n a n d	 	\nd em onstr a te d  c o m pete n cie s in  A gile	 	\nm eth o d olo g y.\nIn nova to r M ile sto n e a t T C S\nCle are d  c o d in g  c h alle n g e o n M ach in e	 	\nLe arn in g	\nwww .enhancv .com	\n	\nPow er ed b y	\nE		q	\n\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;	\n&bull;\n&bull;\n&amp;\n\n',0,0,NULL,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-22 06:59:41',0),(445,104,1,NULL,NULL,'1_104_1815087197669e035d62f13_Arjun_V_Menon_-_HMI_Developer.pdf','Arjun V Menon - HMI Developer.pdf','. \n. \n 	 	\nARJUN V MENON	 	\nSoftware Engineer 	 	\n +91 9744816460	  	\n\ncom	 	 	\n https://www.linkedin.co\nm/in/arjun	-menon	-	\n937159141/	 	 	\n 	\n 	\nAn  inspired  android  developer  dedicated  to  continuously  developing, \nimplementing,  and  adopting  new  technologies  to  maximize  development \nefficiency  and  produce  innovative  applications.  I  have  a  total  technical \nexperience of more than 4 years &amp; passionate to	wards solving problems. 	 	\n \n 	Skills	 	\n Native Android Development ( Java , Kotlin) 	 	 \n JSON, RESTful web services. 	 	 \n Retrofit, Volley, Coroutines. 	 	 \n SQL Lite, Room DB, MySql, Shared Preferences. 	 	 \n Markup language (XML). 	 	 \n Version Control (Git, Bitbuket, SVN, Gerrit). 	 	 \n Project Management (Jira, Jenkins, Polarion, Confluence). 	 	 \n AOSP 	 	 \n Android HMI 	 	 	 	\n 	\nWork History	 	\n Senior Software Enginner	 	\nTata Elxsi, Bangalore	 	\n Worked on Android Map application using Mapbox for automotive 	 	\n Worked on Telematics CV2X project, In Android Telematics Service \nApplication. 	 	\n Testing and Debugging on Hardware for Android Telematics. 	 	\n Worked on Android HMI for Launcher App. 	 	\n Skills: Jira 	- Confluence 	- Polarion 	- Gerrit 	- AOSP 	- BinderIPC 	- Android 	\nFramework 	- Java 	- Mqtt 	- AIDL 	 	\n Programmer Analyst	 	\nCognizant, Bangalore	 	\n Worked on Google Play Protect Project. 	 	\n Worked on android security and android reverse engineering. 	 	\n Worked on Identifying security threats and malware in Playstore. 	 	\n Worked Android Malware analysis. 	 	\n Skills: Jira 	- Android Studio 	- Reverse Engineering 	- Java 	- Kotlin 	 	\n 	\n2022	-11 	- Current	 	\n2021	-09 	- 2022	-11	 	\narjunvenumenon@gmail.\n\n. \n. 	\n Software Engineer	 	\nQuest Global, Trivandrum	 	\n Software development in Android Automotive HMI 	 	\n Worked on Phonebook team of HMI, Version control using SVN. 	 	\n Developed features for Phonebook and Contacts of HMI. 	 	\n Testing and debugging of Build in Hardware. 	 	\n Skills: Jira 	- Gerrit 	- AOSP 	- BinderIPC 	- Android Studio 	- AIDL 	 	\n Software Engineer	 	\nAutocon India Pvt. Ltd, Kochi	 	\n Developed new features for proprietary Android applications used on \nphones and tablets; worked with developers to develop toptier solutions \nfor restaurant and retail customers 	 	\n Uses Coroutines for long	-running tasks (database and network access) 	 	\n Maintaine	d long and short	-range deliverables with product 	\nmanagement team according to product roadmaps 	 	\n Created custom mobile applications using native technologies 	 	\n Database access through Room persistence library. 	 	\n Android Developer	 	\nTradeasy Technologies, Kannur	 	\n Created custom mobile applications for retail customers 	 	\n Documented architecture and implementation for internal and external \nconsumption 	 	\n Applied various techniques to locate bugs and errors in applications 	 	\n Designed and built sales applications for Android platform 	 	\n Provided dedicated support and timely issue resolution to clients \nfollowing successful app launch. 	 	 	\n 	\nEducation	 	\n B-tech: 	Electronics And Communication Engineering	 	\nKannur 	University 	- Kannur	 	\nPercentage: 63 	 	\n 12th: 	Science	 	\nChapparapadava HSS 	- Kannur	 	\nPercentage: 70% 	 	\n 10th Std	 	\nChapparapadava HSS 	- Kannur	 	\nPercentage: 85% 	 	 	\n 	\nAccomplishments	 	\n  Conducted Android App Development workshops in various colleges 	 	\n Participated in workshop on Internet of Things by Innovians Technologies 	 	\n Participated in Cannovation 2018 startup exhibition of Kannur University 	 	\n 	 	 	\n. 	\n2021	-04 	- 2021	-09	 	\n2020	-08 	- 2021	-04	 	\n2019	-03 	- 2020	-08	 	\n2014	-07 	- 2018	-05	 	\n2011	-06 	- 2013	-03	 	\n2010	-06 	- 2011	-03',0,0,NULL,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-22 06:59:41',0),(446,104,1,NULL,NULL,'1_104_1272063015669e035d63168_Gowtham_Mohanraj_HMI_Developer_Resume_1_(1).pdf','Gowtham    Mohanraj HMI Developer Resume 1 (1).pdf','ED U C ATIO N\nG OW TH AM  SA SSIS TA N T M AN AG ER	 	C O NTA C T+91 6 383311446go w tth am s6 @ gm ail.c o m ch en naiS K IL LS	Pro je ct M an ag e m en t	 	\nT eam work\nT im e M an ag e m en t\nLe ad ers h ip\nE ffe ctiv e  C om mun ic a tio n\nC rit ic a l  T hin kin g	\nV ELA LA R C O LLE G E O F\nEN GIN EER IN G A N D T EC H NO LO GY\nEle ctr o nic s &amp;  C om mun ic a tio n -  7 5%2 010  -  2 01 32009PO NNU M ATR IC U LA TIO N H R S E C\nSC H O OL\nHig h er s e co nd ary  e d uca tio n -  8 5%W ORK  E X PER IE N CEPR O JE C TS	 	P R O FIL EDyn am ic   an d  in no vativ e   in fo ta in m en t  sy ste m  exp ert  w it h  o ve r  4  ye ars   o f\nexp erie n ce   in   deve lo pin g  an d   im ple m en tin g  cu ttin g-e d ge   in   au to m otiv e\nin d ustr y  a n d  in fo rm atio n s y ste m s.  O ve r a ll  1 0 y e ars  o f e xp erie n ce  in  S o ft w are\nin d ustr y .  P ro ve n  a b ili t y  to  le ad  c ro ss-fu n ctio nal  te am s  a n d  m an ag e  p ro je cts\nfr o m  co nce p t  to   co m ple tio n,  en su rin g  se am le ss  in te gra tio n  of  m ult im ed ia ,\nn av ig atio n,  an d   co nnectiv it y   fe atu re s.  Adep t  at  le ve ra g in g  ad van ce d\nte ch no lo gie s t o  e n han ce  u se r e xp erie n ce  a n d  d riv e  m ark e t c o m petit iv e n ess.\n2 023 -  P R ESE N TM on tr a  - T I c le an  m ob il it y  p riv ate  l im it e dA ssis ta n t m an ag er	 	\nP ro d uct d eve lo p m en t &amp;  R eq uir e m en ts  h an d lin g	  -  C en tr a l R &amp; D	The  cre atio n   an d   exe cu tio n   of  m an ual  te st  ca se s  fo r  all  m od ule s  an d\nap plic a tio n s o f v e h ic le  t e le m atic s.\nT he c re atio n  o f  v e h ic le  a le rts  a n d  in fo rm atio n  th ro u g h th e te le m atic s c lo u d\nfo r m ob il e  a p plic a tio n s a n d  O EM .\nFu n ctio n al  a n d  in te g ra tio n  te stin g  o f  a p plic a tio n  fe atu re s a t  b oth  th e te st\nb en ch  a n d  v e h ic le  l e ve ls  u sin g  a  C AN  A naly ze r.\nC usto m iz in g  th e te le m atic s d ash b oard  a n d  m an ag in g  it   a cco rd in g  to  re al-\nt im e f ie ld  i s su es a n d  s e rv ic e  r e q uir e m en ts .\nD eve lo p m en t  te stin g  o f  v e h ic le  fe atu re s s u ch  a s g eo fe n cin g ,  liv e  tr a ckin g ,\nan d  v e h ic le  a le rts  a n d  A nd ro id  d eve lo p m en t i n  i n fo ta in m en t s y ste m .\nTele m atic s p erfo rm an ce  a n aly sis  t h ro u g h v a rio u s c o m petit iv e  b en ch m ark in g .\nSu p port t h e v e n d or s e le ctio n  p ro ce ss a n d  m an ag e v e n d ors  t o  e n su re  t im ely\nd eliv e ry  a n d  t h e r e q uir e d  q ualit y .\nG ood  k n ow le d ge a n d  u n d ers ta n d in g  o f  E V  a rc h it e ctu re  w it h  m ult ip le  C AN\nnetw ork s i n  t h e v e h ic le .\nP re p ara tio n   of  8D  re p orts   in   co ord in atio n   w it h   pro d uctio n   an d   cro ss-\nfu n ctio n al t e am s t o  s o lv e  c rit ic a l q ualit y  i s su es.\nE xp ertis e  i n  p re p arin g  S co p e o f R eq uir e m en ts  d ocu m en ts .\nT eam  s iz e  :  2\nI n it ia tin g  a n d  im ple m en tin g  U D S p ro to co l  a cro ss  a ll  ve h ic le  d ow nstr e am\nE C Us, c o n sid erin g  a ll a sp ects  a n d  v e rtic e s.\nS u p portin g  t h e d eve lo p m en t o f a  s in g le  c lo u d  p la tfo rm  f o r v a rio u s t e le m atic s\nu n it s .\nA ctiv e ly  in vo lv e d  in  t h e im ple m en ta tio n  a n d  v a lid atio n  o f	  t e le m atic s f e atu re s	\nsu ch  a s :\n D eep  l o g ger -	  V erif ic a tio n  a n d  v a lid atio n  o f e ach  v e h ic le  d ata  p oin t.	1.\n D eep  u p d ate r -	  D eve lo p m en t o f U D S p ro to co l i n  a ll E C Us t o  s u p port	\nF ir m ware  O ve r-T he-A ir  	(F O TA	) u p d ate s.	\n2.\nD eep  c o m man d er -  D eve lo p m en t o f v e h ic le  I m m ob il iz a tio n  s y ste m s.	3.	\nT O OLS	 	CA N oe\nC A N an aly ze r\nB us M aste r\nP -C A N\nJT A G\n\nDoco m o I n te rto u ch  I n d ia  L td  ,  B an g alo re , I n d ia	 	IT  S u p port E n g in eer	M S O utlo ok c o n fig ura tio n , b acku p  ,  r e sto re  a n d  t r o u b le sh ootin g .\nM an ag e D om ain  e n vir o n m en t f o r t w o s it e s t h ro u g h r e m ote  s u p port.\nW ir e le ss n etw ork in g  i n  D om ain  e n vir o n m en t.\nA sse m blin g , h ard w are  u p  g ra d atio n  a n d  t r o u b le sh ootin g .\nPro vid in g  s u p port t o  i n sta llin g  O S f o r t h e u se r m ach in es a n d  s e rv e rs .	\n G em alt o  M id dle  E ast F Z  L LC , D ub ai, U .A .E	                              	2 016 - 2 020	 	 2013- 2 016	 	IS S/N etw ork  a n d  S u p port E n g in eer	R esp on sib le   fo r	   	LA N  an d  W AN  n etw ork   op era tio n s,  sy ste m  d is a ste r	\nre co ve ry ,  data b ase   an d   sy ste m  backu p ,  Pla ye d   ke y  ro le   in   backu p\nm an ag em en t o f I T  d ep artm en t.\nC IS C O   :  Rou te rs ,  Sw it c h es,  Serv e r,  wir e le ss  devic e s,  an d   Acce ss\nte ch nolo g ie s.\nS tr o n g  t r o u b le sh ootin g  s k il ls  o n  L A N , W AN  a n d  W ir e le ss t e ch nolo g ie s.\nM an ag ed  u se r a cco u n ts  w it h  ( A D ) A ctiv e  D ir e cto ry  f o r t h e u se rs  t o  p ro vid e\ncre atio n ,  d ele tio n ,  u se r p erm is sio n  a n d  m an ag ed  u se r s e cu rit y  w it h  g ro u p\np olic ie s\nG ood  e xp erie n ce  i n  i n sta llin g  a n d  m ain ta in in g  t h e v ir tu al w ork sta tio n s	\n        	( E 2C )\nH an d lin g  P K I a n d  V PN  b ase d  s e cu rit y  i s su es.  p erfo rm  t r o u b le sh ootin g  e n d -\nto -e n d  b etw een  t h e s it e s.  P ro vid in g  t r a in in g  fo r t h e u se rs  t o  c o n fig ure  t h e\nce rtif ic a te  a n d  s e cu re  d om ain  w it h  t w o f a cto r a u th en tic a tio n .\nP erfo rm in g   Reg is tr a tio n   an d   Auth orit y   dutie s  to   in clu d e  ce rtif ic a te\ne n ro lm en t a n d  r e vo ca tio n .\nM an ag ed  A cce ss C on tr o l  S yste m  to  m ain ta in  a ll  th e b ad ges o f  u se rs  a n d\nvis it o rs .  T o p ro vid e p erm is sio n s fo r  th e re co m men d ed  d ep ortm en t  a n d\nse cu re d  a re a.\nIn sta llin g  G em alt o  B om gar  a p plic a tio n  fo r  a ll  th e m ach in es in  p ro d uctio n\nare a.	\nK in g sto n  I n fo  s o lu tio n s,B an g alo re .	                                       	2 020 -	  2 023	            	\n        \n       	 T est E n g in eer	\n 	\nperfo rm in g  v a lid atio n  o n  i n fo ta in m en t f e atu re s i n  a ll p ossib le  c o n d it io n s.\nV alid atin g  d if fe re n t m od ule s l ik e  U SB ,F M ,H M I a n d  P hon e	 	\nP erfo rm in g  th e ra p id  v a lid atio n  te sts   fo r  a ll  te st  c o n d it io n s  a n d  in va lid\nc a se s	 	\nP re p arin g  s o ft w are  v a lid atio n  s ta tu s r e p ort .\nD eve lo p m en t a n d  v a lid atio n  o f i n te g ra te d  T ele m atic s a n d  C lu ste r.	\n 	\nI h ave  a ctiv e ly  in it ia te d  a n d  d eve lo p ed  F O TA  im ple m en ta tio n  in  3 W  ( L 5 M ,\nL5 N ),  T ra cto r,  S C V,  a n d  H CV.  S u cce ssfu lly ,  I  h ave  g iv e n  a  d em o w it h  t w o\nve rtic e s.	 	\nI p ro p ose d  F O TA  e n tr y -le ve l c o n d it io n s a n d  c h ecklis ts  b ase d  o n  i n d iv id ual\nE C U a n d  v e h ic le  b eh avio u r,  e n su rin g  n o im pact o n  t h e e n d  c u sto m er a n d\nm ain ta in in g  v e h ic le  p erfo rm an ce .	\nL A N G UAG ES	Engli s h	 	\nIE LT S w it h  b an d  6\nT am il	 	\nS TA C K H O LD ER S	Sib ro s ,  N esh  ,  C onnectm  ,\nP ric o l  ,  E le ctr o nik a ,  A W EV ,\nSo na C om sta r ,E xid e	 	\n      \nO TH ER  I N FO	Passp o rt n o  - K 2849988	 	\nH obbie s - S ke tc h in g &amp;\nG ard en in g	 	\nT han ks	 	G O W TH AM  S A KTH IV EL	,  C hen nai,  2 4 J u n e 2 024',0,0,NULL,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-22 06:59:41',0),(447,104,1,NULL,NULL,'1_104_1238480217669e035d635c1_HMI_Developer-Shailendra_Semwal.docx','HMI Developer-Shailendra Semwal.docx','\r\nShailendra Semwal\r\n# 3047 Janapriya Havens\r\nYalanka, Bangalore-560065)\r\n✉E-mail Id  shailendarsemwal@gmail.com☎Contact No +91-8548074898\r\n  LinkedIn: https://www.linkedin.com/in/shailendrasemwalCareer Objective:\r\nTo work with maximum potential in a challenging and dynamic environment, with an opportunity of working with diverse group of people and enhancing my professional skills with learning and experience for career growth.\r\nExperience Summary:\r\nWith 8 years of experience in Android Application Development, Android Automotive, and AMS Engineering Application Support, I bring a wealth of expertise to my roles. I have working navigation systems for Scania trucks, kitchen management apps for restaurant chains, and provided critical support for engineering applications at Black &amp;amp; Veatch.\r\nTechnical Skills:\r\nTools &ndash; Git SVN, ADB tools, QFIL Tools, AOSP\r\nLanguage &ndash;  JAVA, Kotlin, Java(Android SDK)\r\nDatabase &ndash; SQLite, MySQL.\r\nTechnology &ndash; Android,AOSPOperating System &ndash; Windows, Mac,LinexIDE &ndash;Android Studio, Eclipse,Microsoft PowerBITicketing Tools- ServiceNow, Jira, Conference, Figma\r\nAreas Of Expertise:\r\n&bull;    Familiar with the Android SDK, Eclipse, Google services and APIs.\r\n&bull;    Knowledge of SQLite, MySQL or Oracle database management system.\r\n&bull;    Proficient in design, problem-solving, and debugging.\r\n&bull;    Proficient with Developer Console in publishing and managing the application.\r\n&bull;    Knowledge in integrating web services, TCP and UDP socket programming.\r\n&bull;    Familiarity with HERE SDK Navigation. \r\n&bull;    Knowledge of Android AOSP Automotive ROM  Development.\r\n&bull;    Knowledge of Android build system.\r\n&bull;    Knowledge of Android boot process Deep Analysis.\r\n&bull;    Knowledge of Car Api, Car Services, VHAL.\r\n&bull;    Knowledge of Car Launcher.\r\n&bull;    Knowledge of Boot Animation.\r\n&bull;    Knowledge of SELinux.\r\n&bull;    Knowledge of Car Settings.\r\n&bull;    Knowledge of System UI.\r\n&bull;    Knowledge of Mobile Penetration Testing of Android Application.\r\nOrganization Experience\r\nEmployer :  L&amp;amp;T Technology Services Ltd\r\nDuration : March 2021 &ndash; Till now\r\nDesignation : Senior Engineer(Android  Developer)\r\n Project Name: -Navigation\r\nClient: Scania \r\nAs part of our Android automotive project, we are developing a navigation app specifically tailored for Scania trucks. This app aims to provide Scania truck drivers with reliable and efficient navigation assistance to optimize their driving experience. Our focus is on creating a user-friendly interface that takes into account the unique requirements of truck drivers, such as vehicle dimensions, weight restrictions, and specific road regulations. The navigation features will include truck-specific routing options, real-time traffic updates, and points of interest relevant to truck drivers, such as rest areas and truck stops. To ensure seamless integration with Scania trucks, we are collaborating closely with the client to understand their specific needs and requirements. Our development process involves leveraging the latest technologies and best practices in Android automotive development to deliver a high-quality and robust navigation solution. By working closely with Scania and leveraging our expertise in Android development, we aim to create a navigation app that enhances the driving experience for Scania truck drivers, improving efficiency and safety on the road. \r\nDDU,RIG,QFIL Tools, AOSP, Gadget Testing, HERE SDK, JIRA, Figma, Coverity static analysis.\r\nEmployer :  Barbeque Nation Hospitality Ltd\r\nDuration : August2020 &ndash; Till now\r\nDesignation : Assistant Manager(Android  Developer)\r\nProject Name:-KDS:( Hospitality Management)\r\nhttps://play.google.com/store/apps/details?id=com.bbqn.relayProject based on Hospitality Management.\r\nProject Name:- Samiksha:( ERP)\r\nhttps://play.google.com/store/apps/details?id=com.app.bbqn.samikshaEmployer :  DFoE (Digital Future of Education Pvt. Ltd.)\r\nDuration : February 2020 &ndash; August2020\r\nDesignation : Android  Research Engineer\r\nProject Name:-FRI:( Hospitality Management)\r\nhttps://play.google.com/store/apps/details?id=com.dfoe.friclienthttps://play.google.com/store/apps/details?id=com.dfoe.frimasterhttps://play.google.com/store/apps/details?id=com.dfoe.frikitchenTechnologies used:&nbsp; JSON, REST, TCP and UDP Socket Programming, SQLite Db.\r\nProject based on Hospitality Management.\r\nPayment Gateway integration using Stripe Payment, PayU.\r\ncommunication protocols Socket.\r\nOrganization Experience\r\nEmployer : Ayshwarya syndicate LLP, Bangalore\r\nDuration :August 2019 &ndash; November 2019\r\nDesignation :Software Developer\r\nProject Name:-Spark IN:\r\nhttps://play.google.com/store/apps/details?id=com.assccl.spark.coopProject based on Banking Consumer Account.\r\nPush notification is implemented using Google Firebase.\r\nPayment Gateway integration using Paytm, Razor pay;\r\nImplemented finger print access security and Device lock security.\r\nHere customer can create Health Insurance, Debit and Credit Cards, Bill Payment.\r\nEmployer :  Technoforte Software (p) Ltd\r\nDuration :  August  2018 &ndash; August  2019\r\nDesignation : Associate Engineer -Software. \r\nClient: Toyota\r\nProject Name:- Polygon:\r\nManufacturer of inventory management &amp;amp; parts tracking systems for real-time inventory control    &amp;amp; tracking serial number, date tracking, trace the movement of materials.\r\nAn Integrated ZebraBarCode scanner, library to scan the barcode.\r\nDesigned the database, UI pattern, data-flow between different modules.\r\nTesting the end to end app, database &amp;amp; web-services.\r\nEmployer : Pronto Software Solutions (p) Ltd, Bangalore\r\nDuration :  November 2016 &ndash; August 2018\r\nDesignation : Associate Software Engineer\r\nProject Name:- Sangama:\r\nClient: Karnataka Forest Department - Belagavi Circle.\r\nhttps://play.google.com/store/apps/details?id=com.pronto.sangamaSangama is an application which is designed to help the officers monitor and Update their work status online/offline.Key Features of the app are as follows: \r\nProject Name:- Cricket Score Pad:\r\nhttps://play.google.com/store/apps/details?id=com.pronto.scorepadCricket Score Pad comes closest to the cricket score card you have filled manually all this years. A scorer&rsquo;s delight, Cricket Score Pad&#039;s neat, clean and intuitive design ensures the cricket score book stays upto-the-ball without you breaking a sweat. Use Cricket Score Pad to score multiple match formats (T20 or ODI) or even custom make your own match.\r\nDesign UI pattern &amp;amp; database.\r\nIdentify and correct fix bugs\r\nEmployer : SPH Vibhava Private (p) Ltd, Bangalore\r\nDuration :  April 2016 &ndash; October 2016\r\nDesignation : Software  Developer\r\nProject Name:- SmartPropertyHunt:\r\nCertified Course\r\n4  month Java &amp;amp; Testing Training In &ldquo;ABC For java &amp;amp; Testing&rdquo; in Bangalore\r\n4 month .Net Technology Training in DUCAT Gwalior.\r\n5 month  C,C++,Core Java Training In GLOBAL ICS Delhi\r\n2 Month  Android Training in Falgee Software Development Pvt Ltd Bangalore.\r\nAcademic Background\r\nYear(s) Qualification  Board/University College/ Institute Percentage\r\n2015  Bachelor of Engineering \r\n(Information Technology) Rajiv Gandhi Proudyogiki   Vishwavidyalaya Bhopal\r\n(Madhya Pradesh)\r\nRustamji Institute of  Technology  BSF Academy Tekanpur Gwalior \r\n(Madhya Pradesh)\r\n 7.75 CGPA \r\n2012           Diploma\r\n(Computer Engineering)\r\nBoard Of Technical Education Delhi(B.T.E) BSF Polytechnic STS \r\nTigri Camp, New Delhi    68.00%\r\nPersonal Details Name : Shailendra Semwal\r\nFather&rsquo;s Name : Prakash Chandra Semwal\r\nMother&rsquo;s Name : Prabha Devi Semwal\r\nDate of birth : 06/11/1992\r\nNationality : Indian\r\nLanguages known : English, Hindi (read, write and speak)\r\nPermanent Address : Tehri Garhwal Uttarakhand \r\nDeclaration I do hereby confirm that the details furnished above are true to the best of my knowledge. I bear the responsibility for the correctness of the above-mentioned particulars.\r\nPlace: Bengaluru\r\nDate: (Shailendra Semwal)\r\n',0,0,NULL,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-22 06:59:41',0),(448,105,1,NULL,NULL,'1_105_1839314745669e035dccde4_Ajay_Chaudhari_-_HMI_Developer.pdf','Ajay Chaudhari - HMI Developer.pdf','/  	\nA JAY  C H AU DH ARI\nSoft w are  D eve lo p er	\narc h au d hari1 4 0 2@ gm ail. c o m	h ttp s:/ /w ww.lin ke d in .c o m /in /a ja y -\nc h au d hari- 0 15 6 ba16 9/	\nK an ag aw a, J a p an	\nS U M MARY\nExp erie n ce d  S oft w are  D eve lo p er w it h  o ve r 4 .3  y e ars  s p ecia liz in g  in  A nd ro id -\nbase d  A uto m otiv e  In fo ta in m en t s y ste m s 	 \n 	\nP ostg re S Q L,  a n d  O doo.  I  a m  e ag er t o  c o lla b ora te  o n a d va n cin g  c u ttin g -e d ge	 	\nte ch no lo g y a n d  d eliv e rin g  e xc e p tio nal  r e su lt s .\nE X PER IE N CE\nOnsit e  D eve lo p m en t E ngin eer\nTata  C on su lt a n cy S erv ic e s L td	\n0 5/2 0 24	&nbsp;	K an ag aw a,  J a p an	\nA ssig ned  F e atu re :  R ad io  	 	\nR ad io ,  P od ca st)\nO nsit e  S PO C f o r R ad io  f e atu re ,  C lie n t c o m mun ic a tio n,  M an ag e S O P /  Q LU	 	\n/ M ain lin e d eliv e rie s f o r N is sa n  V eh ic le s,  B ug  t r ia g in g  a n d  f ix in g\nW ork in g  w it h  c ro ss-fu n ctio nal  t e am s s u ch  a s X peri,   L G ,  G oog le ,  B osc h ,	 	\netc .\nT e ch S ta ck:  A nd ro id ,  K otlin ,  J a va ,  A O SP,  A ASP	\nS yste m s E ngin eer\nTata  C on su lt a n cy S erv ic e s L td	\n0 1/2 0 22 	&nbsp;	G an d hin ag ar,  In d ia	\nN is sa n  IV I  f e atu re s:  T e le m atic s 	 	\nC onnm an  \nF i,   a n d  H ots p ot).\nH M I  d eve lo p m en t t h at w ork s w it h  A O SP a n d  H AL p ro vid ed  b y L G  a n d	 	\nB osc h ,  f e atu rin g  1 0 0\nB uilt  p yth o n s c rip t t h at r e d uce d  S ST R g en era tio n t im e b y 8 0 %\nTe ch S ta ck:  A nd ro id ,  K otlin ,  J a va	\nJ r. A pplic atio n  E ngin eer\nTin yE R P P vt. L td	\n1 0 /2 0 20  	&nbsp;	A hm ed ab ad ,  In d ia	\nO doo:  E R P B usin ess S uit e  o f o p en -s o urc e  b usin ess a p ps c o ve rin g  C RM ,	 	\neC om merc e ,  A cco un tin g ,  In ve n to ry ,  P O S e tc\nD eve lo p m en t o f c u sto m  m od ule s a s p er c lie n ts &#039;  n eed s.  A ddre sse d  m ain ly	 	\ng en eric  &amp;  s p ecif ic  is su e s d urin g  d ata b ase  u p gra d es.  1  g en eric  f ix  c a n	 	\nre so lv e  r o ug hly  1 0  C lie n ts  d ata b ase  u p gra d e is su e s\nTe ch S ta ck:  P yth o n,  P ostg re sq l,   J S ,  U bun tu	\nP yth on /O doo D eve lo p er\nBro w se ln fo  P vt. L td	\n0 3 /2 0 20  	&nbsp;	A hm ed ab ad ,  In d ia	\nD eve lo p ed  C usto m  L ib ra ry  m an ag em en t O doo A pp,  P ro d uct Q ualit y	 	\nA ssu ra n ce  A pp u sin g  g en eric  m od ule s\nC ontr ib ute d  t o  e xis tin g  O doo A pps lik e  B atc h  P ay m en t d urin g  in vo ic in g\nTe ch S ta ck:  P yth o n,  J S ,  O doo	\nE D U CATIO N\nBach elo r o f E ngin eerin g\nSA L E n g in eerin g  A nd  T e ch nic al I n stit u te , G TU	\n04 /2 0 17  	&nbsp;	A hm ed ab ad ,  In d ia	\nGP A	\n8.2 7	10	\nC om pute r E ng in eerin g  M ajo r	\nS KIL LS\nTe ch S ta ck\nA nd ro id	J av a	K otlin	P yth on	\nO doo	P ostg re sq l	M atp lo tL ib	\nN um py	O pen C V	Pan d as	S eab orn	\nS kle arn	G it	\nT e ch nic al  E xp ert is e\nA uto m otiv e  I n fo ta in m en t f o r C CS\nER P S oft w are  	M ac h in e L e arn in g	\nP R O JEC TS\nAuto m atic  A tte n dan ce  S yste m	\nhttp s:/ /g it h ub .c o m /A jja rv is /A uto m atic _A tte n d an ce _S\ny ste m	\nF ace  R eco g nit io n-b ase d  A uto m atic  A tte n d an ce	 	\nS yste m  u sin g  C CTV  f o r r e al- tim e s tu d en t	 	\natte n d an ce .	\nTe ch S ta ck:  P yth o n,  T kin te r,  X lr d ,  X ls x w rit e r,	 	\nO pen C v	\nIn diM on ey : A n E xp en se  T ra cke r A pp	\nhttp s:/ /g it h ub .c o m /A jja rv is /In d iM oney	\nA nd ro id  E xp en se  T ra cke r A pp f o r e ffic ie n tly	 	\nm onit o rin g  o f e xp en se  a n d  in co m e w it h  le ss e ffo rts	\nT e ch S ta ck:  A nd ro id ,  S Q Lit e ,  G oog le  M L k it	\nA C H IE V EM EN TS	\nPala d in  o f t h e I V IT  C ham pio n s\nRece iv e d  b eca u se  o f h ig h p erfo rm an ce  o n	 	\ncrit ic a l  p hase  o f t h e p ro je ct\nD ig it a l M ile sto n e a t T C S\nCle are d  c o d in g  c h alle n g es in  P yth o n a n d	 	\nd em onstr a te d  c o m pete n cie s in  A gile	 	\nm eth o d olo g y.\nIn nova to r M ile sto n e a t T C S\nCle are d  c o d in g  c h alle n g e o n M ach in e	 	\nLe arn in g	\nwww .enhancv .com	\n	\nPow er ed b y	\nE		q	\n\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;	\n&bull;\n&bull;\n&amp;\n\n',0,0,NULL,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-22 06:59:41',0),(449,106,1,4,NULL,'1_106_1949089133669e0360dbbee_Ajay_Chaudhari_-_HMI_Developer.pdf','Ajay Chaudhari - HMI Developer.pdf','/  	\nA JAY  C H AU DH ARI\nSoft w are  D eve lo p er	\narc h au d hari1 4 0 2@ gm ail. c o m	h ttp s:/ /w ww.lin ke d in .c o m /in /a ja y -\nc h au d hari- 0 15 6 ba16 9/	\nK an ag aw a, J a p an	\nS U M MARY\nExp erie n ce d  S oft w are  D eve lo p er w it h  o ve r 4 .3  y e ars  s p ecia liz in g  in  A nd ro id -\nbase d  A uto m otiv e  In fo ta in m en t s y ste m s 	 \n 	\nP ostg re S Q L,  a n d  O doo.  I  a m  e ag er t o  c o lla b ora te  o n a d va n cin g  c u ttin g -e d ge	 	\nte ch no lo g y a n d  d eliv e rin g  e xc e p tio nal  r e su lt s .\nE X PER IE N CE\nOnsit e  D eve lo p m en t E ngin eer\nTata  C on su lt a n cy S erv ic e s L td	\n0 5/2 0 24	&nbsp;	K an ag aw a,  J a p an	\nA ssig ned  F e atu re :  R ad io  	 	\nR ad io ,  P od ca st)\nO nsit e  S PO C f o r R ad io  f e atu re ,  C lie n t c o m mun ic a tio n,  M an ag e S O P /  Q LU	 	\n/ M ain lin e d eliv e rie s f o r N is sa n  V eh ic le s,  B ug  t r ia g in g  a n d  f ix in g\nW ork in g  w it h  c ro ss-fu n ctio nal  t e am s s u ch  a s X peri,   L G ,  G oog le ,  B osc h ,	 	\netc .\nT e ch S ta ck:  A nd ro id ,  K otlin ,  J a va ,  A O SP,  A ASP	\nS yste m s E ngin eer\nTata  C on su lt a n cy S erv ic e s L td	\n0 1/2 0 22 	&nbsp;	G an d hin ag ar,  In d ia	\nN is sa n  IV I  f e atu re s:  T e le m atic s 	 	\nC onnm an  \nF i,   a n d  H ots p ot).\nH M I  d eve lo p m en t t h at w ork s w it h  A O SP a n d  H AL p ro vid ed  b y L G  a n d	 	\nB osc h ,  f e atu rin g  1 0 0\nB uilt  p yth o n s c rip t t h at r e d uce d  S ST R g en era tio n t im e b y 8 0 %\nTe ch S ta ck:  A nd ro id ,  K otlin ,  J a va	\nJ r. A pplic atio n  E ngin eer\nTin yE R P P vt. L td	\n1 0 /2 0 20  	&nbsp;	A hm ed ab ad ,  In d ia	\nO doo:  E R P B usin ess S uit e  o f o p en -s o urc e  b usin ess a p ps c o ve rin g  C RM ,	 	\neC om merc e ,  A cco un tin g ,  In ve n to ry ,  P O S e tc\nD eve lo p m en t o f c u sto m  m od ule s a s p er c lie n ts &#039;  n eed s.  A ddre sse d  m ain ly	 	\ng en eric  &amp;  s p ecif ic  is su e s d urin g  d ata b ase  u p gra d es.  1  g en eric  f ix  c a n	 	\nre so lv e  r o ug hly  1 0  C lie n ts  d ata b ase  u p gra d e is su e s\nTe ch S ta ck:  P yth o n,  P ostg re sq l,   J S ,  U bun tu	\nP yth on /O doo D eve lo p er\nBro w se ln fo  P vt. L td	\n0 3 /2 0 20  	&nbsp;	A hm ed ab ad ,  In d ia	\nD eve lo p ed  C usto m  L ib ra ry  m an ag em en t O doo A pp,  P ro d uct Q ualit y	 	\nA ssu ra n ce  A pp u sin g  g en eric  m od ule s\nC ontr ib ute d  t o  e xis tin g  O doo A pps lik e  B atc h  P ay m en t d urin g  in vo ic in g\nTe ch S ta ck:  P yth o n,  J S ,  O doo	\nE D U CATIO N\nBach elo r o f E ngin eerin g\nSA L E n g in eerin g  A nd  T e ch nic al I n stit u te , G TU	\n04 /2 0 17  	&nbsp;	A hm ed ab ad ,  In d ia	\nGP A	\n8.2 7	10	\nC om pute r E ng in eerin g  M ajo r	\nS KIL LS\nTe ch S ta ck\nA nd ro id	J av a	K otlin	P yth on	\nO doo	P ostg re sq l	M atp lo tL ib	\nN um py	O pen C V	Pan d as	S eab orn	\nS kle arn	G it	\nT e ch nic al  E xp ert is e\nA uto m otiv e  I n fo ta in m en t f o r C CS\nER P S oft w are  	M ac h in e L e arn in g	\nP R O JEC TS\nAuto m atic  A tte n dan ce  S yste m	\nhttp s:/ /g it h ub .c o m /A jja rv is /A uto m atic _A tte n d an ce _S\ny ste m	\nF ace  R eco g nit io n-b ase d  A uto m atic  A tte n d an ce	 	\nS yste m  u sin g  C CTV  f o r r e al- tim e s tu d en t	 	\natte n d an ce .	\nTe ch S ta ck:  P yth o n,  T kin te r,  X lr d ,  X ls x w rit e r,	 	\nO pen C v	\nIn diM on ey : A n E xp en se  T ra cke r A pp	\nhttp s:/ /g it h ub .c o m /A jja rv is /In d iM oney	\nA nd ro id  E xp en se  T ra cke r A pp f o r e ffic ie n tly	 	\nm onit o rin g  o f e xp en se  a n d  in co m e w it h  le ss e ffo rts	\nT e ch S ta ck:  A nd ro id ,  S Q Lit e ,  G oog le  M L k it	\nA C H IE V EM EN TS	\nPala d in  o f t h e I V IT  C ham pio n s\nRece iv e d  b eca u se  o f h ig h p erfo rm an ce  o n	 	\ncrit ic a l  p hase  o f t h e p ro je ct\nD ig it a l M ile sto n e a t T C S\nCle are d  c o d in g  c h alle n g es in  P yth o n a n d	 	\nd em onstr a te d  c o m pete n cie s in  A gile	 	\nm eth o d olo g y.\nIn nova to r M ile sto n e a t T C S\nCle are d  c o d in g  c h alle n g e o n M ach in e	 	\nLe arn in g	\nwww .enhancv .com	\n	\nPow er ed b y	\nE		q	\n\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;	\n&bull;\n&bull;\n&amp;\n\n',2,2,1,'Recruiter','SL','N',14,1,'2024-09-22 11:34:14','2024-09-19 11:34:14',0,NULL,NULL,'2024-07-22 06:59:44',0),(450,106,1,4,NULL,'1_106_1666907836669e0360dc15a_Arjun_V_Menon_-_HMI_Developer.pdf','Arjun V Menon - HMI Developer.pdf','. \n. \n 	 	\nARJUN V MENON	 	\nSoftware Engineer 	 	\n +91 9744816460	  	\n\ncom	 	 	\n https://www.linkedin.co\nm/in/arjun	-menon	-	\n937159141/	 	 	\n 	\n 	\nAn  inspired  android  developer  dedicated  to  continuously  developing, \nimplementing,  and  adopting  new  technologies  to  maximize  development \nefficiency  and  produce  innovative  applications.  I  have  a  total  technical \nexperience of more than 4 years &amp; passionate to	wards solving problems. 	 	\n \n 	Skills	 	\n Native Android Development ( Java , Kotlin) 	 	 \n JSON, RESTful web services. 	 	 \n Retrofit, Volley, Coroutines. 	 	 \n SQL Lite, Room DB, MySql, Shared Preferences. 	 	 \n Markup language (XML). 	 	 \n Version Control (Git, Bitbuket, SVN, Gerrit). 	 	 \n Project Management (Jira, Jenkins, Polarion, Confluence). 	 	 \n AOSP 	 	 \n Android HMI 	 	 	 	\n 	\nWork History	 	\n Senior Software Enginner	 	\nTata Elxsi, Bangalore	 	\n Worked on Android Map application using Mapbox for automotive 	 	\n Worked on Telematics CV2X project, In Android Telematics Service \nApplication. 	 	\n Testing and Debugging on Hardware for Android Telematics. 	 	\n Worked on Android HMI for Launcher App. 	 	\n Skills: Jira 	- Confluence 	- Polarion 	- Gerrit 	- AOSP 	- BinderIPC 	- Android 	\nFramework 	- Java 	- Mqtt 	- AIDL 	 	\n Programmer Analyst	 	\nCognizant, Bangalore	 	\n Worked on Google Play Protect Project. 	 	\n Worked on android security and android reverse engineering. 	 	\n Worked on Identifying security threats and malware in Playstore. 	 	\n Worked Android Malware analysis. 	 	\n Skills: Jira 	- Android Studio 	- Reverse Engineering 	- Java 	- Kotlin 	 	\n 	\n2022	-11 	- Current	 	\n2021	-09 	- 2022	-11	 	\narjunvenumenon@gmail.\n\n. \n. 	\n Software Engineer	 	\nQuest Global, Trivandrum	 	\n Software development in Android Automotive HMI 	 	\n Worked on Phonebook team of HMI, Version control using SVN. 	 	\n Developed features for Phonebook and Contacts of HMI. 	 	\n Testing and debugging of Build in Hardware. 	 	\n Skills: Jira 	- Gerrit 	- AOSP 	- BinderIPC 	- Android Studio 	- AIDL 	 	\n Software Engineer	 	\nAutocon India Pvt. Ltd, Kochi	 	\n Developed new features for proprietary Android applications used on \nphones and tablets; worked with developers to develop toptier solutions \nfor restaurant and retail customers 	 	\n Uses Coroutines for long	-running tasks (database and network access) 	 	\n Maintaine	d long and short	-range deliverables with product 	\nmanagement team according to product roadmaps 	 	\n Created custom mobile applications using native technologies 	 	\n Database access through Room persistence library. 	 	\n Android Developer	 	\nTradeasy Technologies, Kannur	 	\n Created custom mobile applications for retail customers 	 	\n Documented architecture and implementation for internal and external \nconsumption 	 	\n Applied various techniques to locate bugs and errors in applications 	 	\n Designed and built sales applications for Android platform 	 	\n Provided dedicated support and timely issue resolution to clients \nfollowing successful app launch. 	 	 	\n 	\nEducation	 	\n B-tech: 	Electronics And Communication Engineering	 	\nKannur 	University 	- Kannur	 	\nPercentage: 63 	 	\n 12th: 	Science	 	\nChapparapadava HSS 	- Kannur	 	\nPercentage: 70% 	 	\n 10th Std	 	\nChapparapadava HSS 	- Kannur	 	\nPercentage: 85% 	 	 	\n 	\nAccomplishments	 	\n  Conducted Android App Development workshops in various colleges 	 	\n Participated in workshop on Internet of Things by Innovians Technologies 	 	\n Participated in Cannovation 2018 startup exhibition of Kannur University 	 	\n 	 	 	\n. 	\n2021	-04 	- 2021	-09	 	\n2020	-08 	- 2021	-04	 	\n2019	-03 	- 2020	-08	 	\n2014	-07 	- 2018	-05	 	\n2011	-06 	- 2013	-03	 	\n2010	-06 	- 2011	-03',2,2,1,'Recruiter','SL','N',14,0,'2024-07-25 07:51:14','2024-07-22 07:51:14',0,NULL,NULL,'2024-07-22 06:59:44',0),(451,106,1,4,NULL,'1_106_801164583669e0360dc35b_Gowtham_Mohanraj_HMI_Developer_Resume_1_(1).pdf','Gowtham    Mohanraj HMI Developer Resume 1 (1).pdf','ED U C ATIO N\nG OW TH AM  SA SSIS TA N T M AN AG ER	 	C O NTA C T+91 6 383311446go w tth am s6 @ gm ail.c o m ch en naiS K IL LS	Pro je ct M an ag e m en t	 	\nT eam work\nT im e M an ag e m en t\nLe ad ers h ip\nE ffe ctiv e  C om mun ic a tio n\nC rit ic a l  T hin kin g	\nV ELA LA R C O LLE G E O F\nEN GIN EER IN G A N D T EC H NO LO GY\nEle ctr o nic s &amp;  C om mun ic a tio n -  7 5%2 010  -  2 01 32009PO NNU M ATR IC U LA TIO N H R S E C\nSC H O OL\nHig h er s e co nd ary  e d uca tio n -  8 5%W ORK  E X PER IE N CEPR O JE C TS	 	P R O FIL EDyn am ic   an d  in no vativ e   in fo ta in m en t  sy ste m  exp ert  w it h  o ve r  4  ye ars   o f\nexp erie n ce   in   deve lo pin g  an d   im ple m en tin g  cu ttin g-e d ge   in   au to m otiv e\nin d ustr y  a n d  in fo rm atio n s y ste m s.  O ve r a ll  1 0 y e ars  o f e xp erie n ce  in  S o ft w are\nin d ustr y .  P ro ve n  a b ili t y  to  le ad  c ro ss-fu n ctio nal  te am s  a n d  m an ag e  p ro je cts\nfr o m  co nce p t  to   co m ple tio n,  en su rin g  se am le ss  in te gra tio n  of  m ult im ed ia ,\nn av ig atio n,  an d   co nnectiv it y   fe atu re s.  Adep t  at  le ve ra g in g  ad van ce d\nte ch no lo gie s t o  e n han ce  u se r e xp erie n ce  a n d  d riv e  m ark e t c o m petit iv e n ess.\n2 023 -  P R ESE N TM on tr a  - T I c le an  m ob il it y  p riv ate  l im it e dA ssis ta n t m an ag er	 	\nP ro d uct d eve lo p m en t &amp;  R eq uir e m en ts  h an d lin g	  -  C en tr a l R &amp; D	The  cre atio n   an d   exe cu tio n   of  m an ual  te st  ca se s  fo r  all  m od ule s  an d\nap plic a tio n s o f v e h ic le  t e le m atic s.\nT he c re atio n  o f  v e h ic le  a le rts  a n d  in fo rm atio n  th ro u g h th e te le m atic s c lo u d\nfo r m ob il e  a p plic a tio n s a n d  O EM .\nFu n ctio n al  a n d  in te g ra tio n  te stin g  o f  a p plic a tio n  fe atu re s a t  b oth  th e te st\nb en ch  a n d  v e h ic le  l e ve ls  u sin g  a  C AN  A naly ze r.\nC usto m iz in g  th e te le m atic s d ash b oard  a n d  m an ag in g  it   a cco rd in g  to  re al-\nt im e f ie ld  i s su es a n d  s e rv ic e  r e q uir e m en ts .\nD eve lo p m en t  te stin g  o f  v e h ic le  fe atu re s s u ch  a s g eo fe n cin g ,  liv e  tr a ckin g ,\nan d  v e h ic le  a le rts  a n d  A nd ro id  d eve lo p m en t i n  i n fo ta in m en t s y ste m .\nTele m atic s p erfo rm an ce  a n aly sis  t h ro u g h v a rio u s c o m petit iv e  b en ch m ark in g .\nSu p port t h e v e n d or s e le ctio n  p ro ce ss a n d  m an ag e v e n d ors  t o  e n su re  t im ely\nd eliv e ry  a n d  t h e r e q uir e d  q ualit y .\nG ood  k n ow le d ge a n d  u n d ers ta n d in g  o f  E V  a rc h it e ctu re  w it h  m ult ip le  C AN\nnetw ork s i n  t h e v e h ic le .\nP re p ara tio n   of  8D  re p orts   in   co ord in atio n   w it h   pro d uctio n   an d   cro ss-\nfu n ctio n al t e am s t o  s o lv e  c rit ic a l q ualit y  i s su es.\nE xp ertis e  i n  p re p arin g  S co p e o f R eq uir e m en ts  d ocu m en ts .\nT eam  s iz e  :  2\nI n it ia tin g  a n d  im ple m en tin g  U D S p ro to co l  a cro ss  a ll  ve h ic le  d ow nstr e am\nE C Us, c o n sid erin g  a ll a sp ects  a n d  v e rtic e s.\nS u p portin g  t h e d eve lo p m en t o f a  s in g le  c lo u d  p la tfo rm  f o r v a rio u s t e le m atic s\nu n it s .\nA ctiv e ly  in vo lv e d  in  t h e im ple m en ta tio n  a n d  v a lid atio n  o f	  t e le m atic s f e atu re s	\nsu ch  a s :\n D eep  l o g ger -	  V erif ic a tio n  a n d  v a lid atio n  o f e ach  v e h ic le  d ata  p oin t.	1.\n D eep  u p d ate r -	  D eve lo p m en t o f U D S p ro to co l i n  a ll E C Us t o  s u p port	\nF ir m ware  O ve r-T he-A ir  	(F O TA	) u p d ate s.	\n2.\nD eep  c o m man d er -  D eve lo p m en t o f v e h ic le  I m m ob il iz a tio n  s y ste m s.	3.	\nT O OLS	 	CA N oe\nC A N an aly ze r\nB us M aste r\nP -C A N\nJT A G\n\nDoco m o I n te rto u ch  I n d ia  L td  ,  B an g alo re , I n d ia	 	IT  S u p port E n g in eer	M S O utlo ok c o n fig ura tio n , b acku p  ,  r e sto re  a n d  t r o u b le sh ootin g .\nM an ag e D om ain  e n vir o n m en t f o r t w o s it e s t h ro u g h r e m ote  s u p port.\nW ir e le ss n etw ork in g  i n  D om ain  e n vir o n m en t.\nA sse m blin g , h ard w are  u p  g ra d atio n  a n d  t r o u b le sh ootin g .\nPro vid in g  s u p port t o  i n sta llin g  O S f o r t h e u se r m ach in es a n d  s e rv e rs .	\n G em alt o  M id dle  E ast F Z  L LC , D ub ai, U .A .E	                              	2 016 - 2 020	 	 2013- 2 016	 	IS S/N etw ork  a n d  S u p port E n g in eer	R esp on sib le   fo r	   	LA N  an d  W AN  n etw ork   op era tio n s,  sy ste m  d is a ste r	\nre co ve ry ,  data b ase   an d   sy ste m  backu p ,  Pla ye d   ke y  ro le   in   backu p\nm an ag em en t o f I T  d ep artm en t.\nC IS C O   :  Rou te rs ,  Sw it c h es,  Serv e r,  wir e le ss  devic e s,  an d   Acce ss\nte ch nolo g ie s.\nS tr o n g  t r o u b le sh ootin g  s k il ls  o n  L A N , W AN  a n d  W ir e le ss t e ch nolo g ie s.\nM an ag ed  u se r a cco u n ts  w it h  ( A D ) A ctiv e  D ir e cto ry  f o r t h e u se rs  t o  p ro vid e\ncre atio n ,  d ele tio n ,  u se r p erm is sio n  a n d  m an ag ed  u se r s e cu rit y  w it h  g ro u p\np olic ie s\nG ood  e xp erie n ce  i n  i n sta llin g  a n d  m ain ta in in g  t h e v ir tu al w ork sta tio n s	\n        	( E 2C )\nH an d lin g  P K I a n d  V PN  b ase d  s e cu rit y  i s su es.  p erfo rm  t r o u b le sh ootin g  e n d -\nto -e n d  b etw een  t h e s it e s.  P ro vid in g  t r a in in g  fo r t h e u se rs  t o  c o n fig ure  t h e\nce rtif ic a te  a n d  s e cu re  d om ain  w it h  t w o f a cto r a u th en tic a tio n .\nP erfo rm in g   Reg is tr a tio n   an d   Auth orit y   dutie s  to   in clu d e  ce rtif ic a te\ne n ro lm en t a n d  r e vo ca tio n .\nM an ag ed  A cce ss C on tr o l  S yste m  to  m ain ta in  a ll  th e b ad ges o f  u se rs  a n d\nvis it o rs .  T o p ro vid e p erm is sio n s fo r  th e re co m men d ed  d ep ortm en t  a n d\nse cu re d  a re a.\nIn sta llin g  G em alt o  B om gar  a p plic a tio n  fo r  a ll  th e m ach in es in  p ro d uctio n\nare a.	\nK in g sto n  I n fo  s o lu tio n s,B an g alo re .	                                       	2 020 -	  2 023	            	\n        \n       	 T est E n g in eer	\n 	\nperfo rm in g  v a lid atio n  o n  i n fo ta in m en t f e atu re s i n  a ll p ossib le  c o n d it io n s.\nV alid atin g  d if fe re n t m od ule s l ik e  U SB ,F M ,H M I a n d  P hon e	 	\nP erfo rm in g  th e ra p id  v a lid atio n  te sts   fo r  a ll  te st  c o n d it io n s  a n d  in va lid\nc a se s	 	\nP re p arin g  s o ft w are  v a lid atio n  s ta tu s r e p ort .\nD eve lo p m en t a n d  v a lid atio n  o f i n te g ra te d  T ele m atic s a n d  C lu ste r.	\n 	\nI h ave  a ctiv e ly  in it ia te d  a n d  d eve lo p ed  F O TA  im ple m en ta tio n  in  3 W  ( L 5 M ,\nL5 N ),  T ra cto r,  S C V,  a n d  H CV.  S u cce ssfu lly ,  I  h ave  g iv e n  a  d em o w it h  t w o\nve rtic e s.	 	\nI p ro p ose d  F O TA  e n tr y -le ve l c o n d it io n s a n d  c h ecklis ts  b ase d  o n  i n d iv id ual\nE C U a n d  v e h ic le  b eh avio u r,  e n su rin g  n o im pact o n  t h e e n d  c u sto m er a n d\nm ain ta in in g  v e h ic le  p erfo rm an ce .	\nL A N G UAG ES	Engli s h	 	\nIE LT S w it h  b an d  6\nT am il	 	\nS TA C K H O LD ER S	Sib ro s ,  N esh  ,  C onnectm  ,\nP ric o l  ,  E le ctr o nik a ,  A W EV ,\nSo na C om sta r ,E xid e	 	\n      \nO TH ER  I N FO	Passp o rt n o  - K 2849988	 	\nH obbie s - S ke tc h in g &amp;\nG ard en in g	 	\nT han ks	 	G O W TH AM  S A KTH IV EL	,  C hen nai,  2 4 J u n e 2 024',2,2,1,'Recruiter','SL','N',14,0,'2024-07-25 07:51:59','2024-07-22 07:51:59',0,NULL,NULL,'2024-07-22 06:59:44',0),(452,106,1,4,NULL,'1_106_1564251179669e0360dc771_HMI_Developer-Shailendra_Semwal.docx','HMI Developer-Shailendra Semwal.docx','\r\nShailendra Semwal\r\n# 3047 Janapriya Havens\r\nYalanka, Bangalore-560065)\r\n✉E-mail Id  shailendarsemwal@gmail.com☎Contact No +91-8548074898\r\n  LinkedIn: https://www.linkedin.com/in/shailendrasemwalCareer Objective:\r\nTo work with maximum potential in a challenging and dynamic environment, with an opportunity of working with diverse group of people and enhancing my professional skills with learning and experience for career growth.\r\nExperience Summary:\r\nWith 8 years of experience in Android Application Development, Android Automotive, and AMS Engineering Application Support, I bring a wealth of expertise to my roles. I have working navigation systems for Scania trucks, kitchen management apps for restaurant chains, and provided critical support for engineering applications at Black &amp;amp; Veatch.\r\nTechnical Skills:\r\nTools &ndash; Git SVN, ADB tools, QFIL Tools, AOSP\r\nLanguage &ndash;  JAVA, Kotlin, Java(Android SDK)\r\nDatabase &ndash; SQLite, MySQL.\r\nTechnology &ndash; Android,AOSPOperating System &ndash; Windows, Mac,LinexIDE &ndash;Android Studio, Eclipse,Microsoft PowerBITicketing Tools- ServiceNow, Jira, Conference, Figma\r\nAreas Of Expertise:\r\n&bull;    Familiar with the Android SDK, Eclipse, Google services and APIs.\r\n&bull;    Knowledge of SQLite, MySQL or Oracle database management system.\r\n&bull;    Proficient in design, problem-solving, and debugging.\r\n&bull;    Proficient with Developer Console in publishing and managing the application.\r\n&bull;    Knowledge in integrating web services, TCP and UDP socket programming.\r\n&bull;    Familiarity with HERE SDK Navigation. \r\n&bull;    Knowledge of Android AOSP Automotive ROM  Development.\r\n&bull;    Knowledge of Android build system.\r\n&bull;    Knowledge of Android boot process Deep Analysis.\r\n&bull;    Knowledge of Car Api, Car Services, VHAL.\r\n&bull;    Knowledge of Car Launcher.\r\n&bull;    Knowledge of Boot Animation.\r\n&bull;    Knowledge of SELinux.\r\n&bull;    Knowledge of Car Settings.\r\n&bull;    Knowledge of System UI.\r\n&bull;    Knowledge of Mobile Penetration Testing of Android Application.\r\nOrganization Experience\r\nEmployer :  L&amp;amp;T Technology Services Ltd\r\nDuration : March 2021 &ndash; Till now\r\nDesignation : Senior Engineer(Android  Developer)\r\n Project Name: -Navigation\r\nClient: Scania \r\nAs part of our Android automotive project, we are developing a navigation app specifically tailored for Scania trucks. This app aims to provide Scania truck drivers with reliable and efficient navigation assistance to optimize their driving experience. Our focus is on creating a user-friendly interface that takes into account the unique requirements of truck drivers, such as vehicle dimensions, weight restrictions, and specific road regulations. The navigation features will include truck-specific routing options, real-time traffic updates, and points of interest relevant to truck drivers, such as rest areas and truck stops. To ensure seamless integration with Scania trucks, we are collaborating closely with the client to understand their specific needs and requirements. Our development process involves leveraging the latest technologies and best practices in Android automotive development to deliver a high-quality and robust navigation solution. By working closely with Scania and leveraging our expertise in Android development, we aim to create a navigation app that enhances the driving experience for Scania truck drivers, improving efficiency and safety on the road. \r\nDDU,RIG,QFIL Tools, AOSP, Gadget Testing, HERE SDK, JIRA, Figma, Coverity static analysis.\r\nEmployer :  Barbeque Nation Hospitality Ltd\r\nDuration : August2020 &ndash; Till now\r\nDesignation : Assistant Manager(Android  Developer)\r\nProject Name:-KDS:( Hospitality Management)\r\nhttps://play.google.com/store/apps/details?id=com.bbqn.relayProject based on Hospitality Management.\r\nProject Name:- Samiksha:( ERP)\r\nhttps://play.google.com/store/apps/details?id=com.app.bbqn.samikshaEmployer :  DFoE (Digital Future of Education Pvt. Ltd.)\r\nDuration : February 2020 &ndash; August2020\r\nDesignation : Android  Research Engineer\r\nProject Name:-FRI:( Hospitality Management)\r\nhttps://play.google.com/store/apps/details?id=com.dfoe.friclienthttps://play.google.com/store/apps/details?id=com.dfoe.frimasterhttps://play.google.com/store/apps/details?id=com.dfoe.frikitchenTechnologies used:&nbsp; JSON, REST, TCP and UDP Socket Programming, SQLite Db.\r\nProject based on Hospitality Management.\r\nPayment Gateway integration using Stripe Payment, PayU.\r\ncommunication protocols Socket.\r\nOrganization Experience\r\nEmployer : Ayshwarya syndicate LLP, Bangalore\r\nDuration :August 2019 &ndash; November 2019\r\nDesignation :Software Developer\r\nProject Name:-Spark IN:\r\nhttps://play.google.com/store/apps/details?id=com.assccl.spark.coopProject based on Banking Consumer Account.\r\nPush notification is implemented using Google Firebase.\r\nPayment Gateway integration using Paytm, Razor pay;\r\nImplemented finger print access security and Device lock security.\r\nHere customer can create Health Insurance, Debit and Credit Cards, Bill Payment.\r\nEmployer :  Technoforte Software (p) Ltd\r\nDuration :  August  2018 &ndash; August  2019\r\nDesignation : Associate Engineer -Software. \r\nClient: Toyota\r\nProject Name:- Polygon:\r\nManufacturer of inventory management &amp;amp; parts tracking systems for real-time inventory control    &amp;amp; tracking serial number, date tracking, trace the movement of materials.\r\nAn Integrated ZebraBarCode scanner, library to scan the barcode.\r\nDesigned the database, UI pattern, data-flow between different modules.\r\nTesting the end to end app, database &amp;amp; web-services.\r\nEmployer : Pronto Software Solutions (p) Ltd, Bangalore\r\nDuration :  November 2016 &ndash; August 2018\r\nDesignation : Associate Software Engineer\r\nProject Name:- Sangama:\r\nClient: Karnataka Forest Department - Belagavi Circle.\r\nhttps://play.google.com/store/apps/details?id=com.pronto.sangamaSangama is an application which is designed to help the officers monitor and Update their work status online/offline.Key Features of the app are as follows: \r\nProject Name:- Cricket Score Pad:\r\nhttps://play.google.com/store/apps/details?id=com.pronto.scorepadCricket Score Pad comes closest to the cricket score card you have filled manually all this years. A scorer&rsquo;s delight, Cricket Score Pad&#039;s neat, clean and intuitive design ensures the cricket score book stays upto-the-ball without you breaking a sweat. Use Cricket Score Pad to score multiple match formats (T20 or ODI) or even custom make your own match.\r\nDesign UI pattern &amp;amp; database.\r\nIdentify and correct fix bugs\r\nEmployer : SPH Vibhava Private (p) Ltd, Bangalore\r\nDuration :  April 2016 &ndash; October 2016\r\nDesignation : Software  Developer\r\nProject Name:- SmartPropertyHunt:\r\nCertified Course\r\n4  month Java &amp;amp; Testing Training In &ldquo;ABC For java &amp;amp; Testing&rdquo; in Bangalore\r\n4 month .Net Technology Training in DUCAT Gwalior.\r\n5 month  C,C++,Core Java Training In GLOBAL ICS Delhi\r\n2 Month  Android Training in Falgee Software Development Pvt Ltd Bangalore.\r\nAcademic Background\r\nYear(s) Qualification  Board/University College/ Institute Percentage\r\n2015  Bachelor of Engineering \r\n(Information Technology) Rajiv Gandhi Proudyogiki   Vishwavidyalaya Bhopal\r\n(Madhya Pradesh)\r\nRustamji Institute of  Technology  BSF Academy Tekanpur Gwalior \r\n(Madhya Pradesh)\r\n 7.75 CGPA \r\n2012           Diploma\r\n(Computer Engineering)\r\nBoard Of Technical Education Delhi(B.T.E) BSF Polytechnic STS \r\nTigri Camp, New Delhi    68.00%\r\nPersonal Details Name : Shailendra Semwal\r\nFather&rsquo;s Name : Prakash Chandra Semwal\r\nMother&rsquo;s Name : Prabha Devi Semwal\r\nDate of birth : 06/11/1992\r\nNationality : Indian\r\nLanguages known : English, Hindi (read, write and speak)\r\nPermanent Address : Tehri Garhwal Uttarakhand \r\nDeclaration I do hereby confirm that the details furnished above are true to the best of my knowledge. I bear the responsibility for the correctness of the above-mentioned particulars.\r\nPlace: Bengaluru\r\nDate: (Shailendra Semwal)\r\n',2,2,1,'Recruiter','SL','N',14,0,'2024-07-25 07:52:44','2024-07-22 07:52:44',0,NULL,NULL,'2024-07-22 06:59:44',0),(453,107,76,48,15,'76_107_48_2027986868669f7783c2764_Sivakumar-R-DL-Latest.pdf','Sivakumar-R-DL-Latest.pdf','Siv a k u m ar R	\nD ata  A naly st	\nsiv ak u m ar.r s e k ara n @ gm ail.c o m	 	+918220825229	 	B an galo re	 	lin ked in .c o m /in /s iv a-k u m ar- 2 5a3 b 1227	 	\nP R O FIL E	\nD eta il- o rie n te d  d ata  a n aly st w it h  a  B .T ech  i n  B io in fo rm atic s.  P ro fic ie n t i n  d ata b ase  m an ag em en t a n d s k ille d  i n  v ario u s\npro gra m min g la n gu ag es,  in clu din g P yth on ,  M yS Q L,  a n d B ase  S A S.  E xp erie n ce d  in  m ach in e le a rn in g,  d eep  le a rn in g,\nd ata  a n aly sis ,  q u alit y  a ssu ra n ce ,  a n d p ro d u cin g e n d-u se r d ocu m en ta tio n .  P artic u la rly  a d ep t a t l e v e ra g in g P ow er B I f o r\nd ata   vis u aliz a tio n   an d  cra ft in g  co m pellin g  narra tiv e s  fo r  m ed ic a l  co n fe re n ce s.  Su cce ssfu lly   dev e lo p ed   m ult ip le\nm ach in e  le a rn in g  an d  deep   le a rn in g  alg o rit h m s  fo r  re g re ssio n   an d  cla ssif ic a tio n   ta sk s,  w it h   a  fo cu s  on   te x t\ncla ssif ic a tio n ,  m ach in e t r a n sla tio n ,  a n d im ag e c la ssif ic a tio n  in  o n co lo gy d is e a se s,  a s d em on str a te d  in  p ro je cts  f o r o u r\npro d u ct F erm a.A I.	\nP R O FE SSIO N AL E X PER IE N CE	\nExce lR  	EdT ech  	Priv a te  	L im it e d	\nD ata  	A naly st\n•L ed  t h e d ev e lo p m en t o f a  p re d ic tiv e  m od el u sin g m ach in e l e a rn in g t o  f o re ca st	 	\nc u sto m er c h urn ,  r e su lt in g i n  a  1 5%  i m pro ve m en t i n  r e te n tio n  r a te s.	\nM ar 2 023 –  p re se n t	\nB an galo re ,	 I n dia	\n•A uto m ate d  d ata  e x tr a ctio n  t a sk s b y s tr e a m lin in g p ro ce sse s w it h  P yth on  a n d S Q L,	 	\nach ie v in g a  w eek ly  t im e s a v in gs o f 1 5 h ou rs .\n•U tiliz e d  P ow er B I t o  v is u aliz e  c o m ple x  d ata ,  f a cilit a tin g e a sie r i n te rp re ta tio n  a n d	 	\nin fo rm ed  d ecis io n -m ak in g b y t h e m an ag em en t.	\nO rb it h ru st 	C on su lt in g 	P riv a te  	L im it e d	\nA sso cia te  	D ata  	A naly st	\n•C on du cte d  m etic u lo u s d ata  e x tr a ctio n  a n d m in in g o f c lin ic a l d ata  f r o m	 	\nC lin ic a lT ria ls .g o v,  e n su rin g c o m pre h en siv e  d ata  c o ve ra g e.	\nJa n  2 022 –  F eb  2 023	\nC hen nai,	 I n dia	\n•E xecu te d  a  t h oro u gh  q u alit y  c h eck  o n  t h e a n aly ze d  d ata  b y c ra ft in g a  c o h ere n t	 	\nsto ry lin e a n d o rg an iz in g i n sig h ts  t h ro u gh  e ffe ctiv e  b u ck etin g.\n•P re p are d  d eta ile d  d ocu m en ta tio n  o u tlin in g t h e s to ry lin e f o r m ed ic a l c o n fe re n ce s,	 	\nfa cilit a tin g t h e d is se m in atio n  o f k ey  f in din gs a n d i n sig h ts  t o  s ta keh old ers .\n•D ev e lo p ed  c u sto m  Q & A c h atb ot F erm a.A I u sin g l a n gch ain  f r a m ew ork  w it h  a d van ce d	 	\nR AG  a p plic a tio n .	\nB io n iv id  	T ech n olo gy	\nD ata  	A naly st 	I n te rn	\nP erfo rm ed  d ata  a n aly sis  o n  N GS u sin g M S e x ce l,  f a stq c,  c u ta d ap t,  f a stp ,  b ow tie ,	 	\nsa m to ols ,  b cft o ols , V EP	\nM ay 2 021 –  N ov 2 021	\nB an galo re ,	 I n dia	\nP R O JE C T S	\nAI/M L 	P ro je cts	\n•D ev e lo p ed  m ult ip le  c u sto m  Q & A c h atb ot u sin g b oth  O pen A I a n d o p en  s o u rc e  m od els  ( G PT,L L A M A 2 ,L L A M A 3 ,	 	\nM is tr a l) .\n•C re a te d  e n d-to -e n d R AG  p ro je ct w it h  O lla m a a n d O pen  A I A PI k ey  u tiliz in g V ecto r D B s u ch  a s C hro m aD B,  F A IS S,	 	\nA str a D B .\n•C re a te d  t e x t c la ssif ic a tio n  u sin g p re  t r a in ed  B ER T  m od el.\n•I m ple m en te d  i m ag e c la ssif ic a tio n  u sin g t r a n sfe r l e a rn in g o n  t h e R esN et m od u le ,  s h ow ca sin g a n  i n novativ e	 	\na p pro ach  t o  l e v e ra g in g p re -tr a in ed  m od els  f o r i m pro ve d  p erfo rm an ce .\n•C re a te d  a  l o gis tic  r e sg re ssio n  m od el t o  d ia b etic s d is e a se  w it h  8 7%  a cc u ra cy.\n•C re a te d  a  S V M  a n d r a n dom  f o re st m od el f o r l e n din g c lu b l o an  p re d ic tio n .	\nS K IL LS	\nPyth on (P an das,  N um py,S ea b o rn ,	 	\nSkle a rn ,T en so rflo w	 	\nN LT K ,P yto rc h ,O pen C V )\nM ach in e  L ea rn in g(D ecis io n  T re e,	 	\nL o gis tic  R eg re ssio n ,  S V M ,  K	 	\nm ea n s,  C lu ste rin g,	 	\nR eco m men datio n  S yste m  e tc )	\nO OPS\nSQ L	\nCN N,  L ST M ,  B ER T,  C BO W ,	 	\nSK IP G RA M ,  Y O LO ,  G AN ,  F o u rie r	 	\nT ra n sfo rm er,  G LO VE,  R AG ,	 	\nLan gch ain ,L LA M A I N DEX\nPow er B I,  B ase  S A S	\nE D UCA T IO N	\nKaru n ya  	In stit u te  	o f 	T ech n olo gy 	a n d 	Scie n ce s	\nB .T ech  	B io in fo rm atic s	\nJu n  2 017 –  M ay 2 021	\nC oim bato re ,	 I n dia',2,2,1,'Candidate',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-23 09:27:31',48),(454,108,76,49,16,'76_108_49_11878669266a24e95ee27f_Amit_Jain_Data_Science_Resume_2024.pdf','Amit_Jain_Data_Science_Resume_2024.pdf','Public	 	\n                	 	\n \n \n     	     	 	\n \n \n \n \n \nPROFILE SUMMARY	 	     	        	PROFESSIONAL EXPERIENCE	 	\n 	\n• 	6.5+  YoE	 	in  Data  Science  roles  across 	\nindustries.	 	\n• 	Thorough  understanding  of  Data  Science \ntools, techniques and technologies in Machine \nLearning,	 Deep  Learning,  Natural  Language 	\nProcessing	 	(NLP)	 	Using	 	Python,  R	 	\nprogramming.	 	\n• 	Thorough  understanding  of  algorithms  like \nRegression, 	Classification,	 	Clustering, 	\nForecasting,  Anomaly  Detection,	 Time  series 	\nModelling etc.	 	\n• 	Sound  knowledge  of  deployment  using  Git, \nDocker, Jenkins, 	Kubernetes, OpenShift	. 	\n• 	Sound knowledge of Azure Machine Learning \nStudio,  AWS  (Amazon  Web  Services)  AI	-ML 	\nServices.	 	\n• 	Extract	ed	 usable  data  from  valuable  data 	\nsources	. 	\n• 	Carr	ied	 out  preprocessing  of  structured  and 	\nunstructured data	. 	\n• 	Analyz	ed	 large amounts of information to find 	\npatterns and solutions.	 	\n• 	Developed  prediction  systems  and  machine \nlearning algorithms.	 	\n• 	Propose	d solutions  and  strategies  to  tackle 	\nbusiness challenges.	 	\n 	\n   	\n• 	Total 1	4+ 	YoE	. 	\n• 	6.5+	 YoE	 in Analytics, Machine Learning, Data 	\nScience.	 	\n• 	7.8 YoE	 in Academics.	 	\n 	\n      	DATA SCIENTIST	 	\n• 	Data Scientist at 	Wipro	, Pune Since 	Mar	 202	2 – 	\nTill date	 	\n• 	Data  Scientist  at  Equations  Work  IT  Services \nPvt. Ltd., Pune Since Sept 2020 	to Feb 2022	 	\n• 	Data Scientist at Cloudmantra, Pune since Mar \n2020 to Sept 2020	 	\n• 	Data Scientist at Open Insights, Pune since Aug \n2019 to Feb 2020.	 	\n• 	Data  Scientist  at  TorcAI  Digital  Media  Private \nLimited, Pune since Feb 2019 to Jul 2019	 	\n• 	Data  Analyst  at  Findability  Sciences, \nAurangabad since Jan 2018 to Jan 2019.	 	\n       	 	\n      	ACADEMICS	 	\n• 	7.8+  YoE	 Teaching 	experience  as  assistant 	\nprofessor  in  engineering  colleges  since  Aug \n2009 to Mar 2017	 	\n 	\nEDUCATIONAL QUALIFICATION	 	     	PROGRAMMING SKILLS & TOOLS	 	\n 	\n• 	PGP	-2018	 	in  Data  Science,  Business 	\nAnalytics & Big Data in association with IBM	 	\nfrom  Aegis  School  o	f Data  Science	, Cyber	 	\nSecurity	 & Tele	communicati	on	,  Mumbai 	\n(M.H.)	 	\n• 	M.Tech.	-2014	 from  university:  B.U.  Bhopal 	\n(M.P.) with 73%	 	\n• 	PGDIT	-2008	 	from  Indian  Institute  of 	\nTechnology,  Kharagpur  (W.B.)  with  6.67 \nCGPA.	 	\n• 	B.E.	-2006	 from  University:  R.G.P.V.  Bhopal 	\n(M.P.) with 73.16%	 	\n 	\n• 	Programming Language:	 Python	, R Programming	 	\n• 	Software Tools/Technologies:	 Machine Learning, 	\nDeep  Learning,  Natural  Language  Processing \n(NLP), AWS 	AI-ML, Azure 	Machine Learning	 	\n• 	Databases:	 MySQL, PostgreSQL	, Db2	 	\n• 	Operating System:	 Windows, Linux/Unix	 	\n• 	Authoring Tools:	 Microsoft Office, Open Office	 	\n• 	BI Tools/ Data Reporting 	Tool:	 Qlik Sense	 	\n• 	Version Control:	 GIT	 	\n• 	Model Deployment Tool:	 Heroku, AWS, Azure	 	\n• 	Cloud platform:	 IBM	, AWS, Azure	, GCP	 	\n• 	Project 	M	anagement 	Too	l: Jira, 	GitHub	 	\n 	\nAmit Jain	 	\nData Scientist	 	\n  amitjain2110@gmail.com	 	\nlinkedin.com/in/amit	-jain	-83292740/	 	\n  youTube/amit	-jain	 	\n  github.com/amitjain2110	 	\n 917	9982256\n\nPublic	 	\nWIPRO	 PROJECTS	:   	 	 	 	 	 	 	 	Mar 2022 	– Till 	date	 	\n \nProject Title: 	Space Insights	 	\nProject Profile: 	To Build AI model to predict about Ideal sharing ratio	. 	\nRoles & Responsibility: 	 	\na. Analysis to build a machine learning model for Ideal sharing ratio.	 	\nTools/Technologies used:	 Python, 	IBM Maximo 	Application Suite	 	\n \nProject Title: 	Tririga Building	 Insights	 	\nProject Profile: 	To Build AI model for 	TBI.	 	\nRoles & Responsibility: 	 	\n    	a. 	Create Anomaly detection model and other AI models for 	OpenShift	 Platform (OCP)	 	\n    	b.  	Successful transition of project from Kubernetes to 	OpenShift	 Platform	 in IBM Cloud	 	\n    	c.     	PostgreSQL to Db2 migration  	 	\n    	d.  	Installation of modules like IoT, cp4d(WML), 	Monitor etc.	 	\n    	e.  	Responsible to run security Scan (Static, Dynamic, 	white source	, mend, PVR, 	twist lock	) 	\n Tools/Technologies used: 	Python, Machine Learning, IBM Maximo 	Application Suite	, IBM 	Cloud,	 Openshift	  	\n        	Cluster, 	Kubernetes	 	\n \nEQUATIONS WORK PROJECTS:	    	 	 	 	 	 	Sept 2020 to Feb 2022	 	\n \nProject Title:	 Sales forecasting and Demand Forecasting	 	\nProject Profile:	 To forecast the sales and revenue of e	-commerce company.	 	\nRoles & Responsibility: 	 	\na. Build the model using LSTM Time Series	 Modelling.	 	\nTools/Technologies used:	 Python, Azure 	Machine Learning	 	\n \nProject Title:	 Prediction of Motor Speed & temperature 	 	\nProject Profile:	 To predict the temperature and deployed it using Power platform.	 	\nRoles & Responsibility: 	 	\n    	a.  	End to end model creation & deployment	 	\n    	b.  	Build the model using 	Random Forest	 regression	 and XGBoost 	regression.	 	\n    	c.  	Enabled to run the project in Azure Power Platform.	 	\nTools/Technologies used:	 Python, Azure Machine	 Learning Studio, Power App, Power Automate	 	\n \nProject Title:	 Prediction /Analysis of Horse Race	 	\nProject Profile: 	 	\na. To predict which horse will win the race.	 Also, 	decide	 the position of all the horses.	 	\nRoles & Responsibility: 	 	\na. 	Responsible for prediction 	of result 	using 	Random 	Forest	 classification	, XGBoost	 classification	 and ANN 	    	\nclassification	 model.	 	\n    	b.  	Requirement collection from client.	 	\n    	c.  	Schedule and planning for project execution. 	 	\nTools/Technologies used:	 Python, Machine 	Learning	 	\n \nProject Title:	 Text summarization of audit files	 	\nProject Profile:	 To read text from 	audit 	reports	(pdf	) and	 summari	ze	 text	 report	. 	\nRoles & Responsibility: 	 	\n    	a. 	Responsible for text extraction	 using AWS Textract 	and summarization	 using BERT	 library	. 	\n    	b.  	 Requirement collection and deployment.	 	\nTools/Technologies 	used:	 Python, NLP, A	mazon	 Machine 	Learning, AWS Textract, 	Amazon S3, 	Git, Docker, 	  	\n       	Jenkins\n\nPublic	 	\nCLOUDMATRA	 PROJECTS	:  	 	 	 	 	 	 	Mar 2020 to Sept 2020	 	\n \nProject Title:	 Specific text extraction of audit reports 	 	\nProject Profile	: To extract specific text and its related	 data with units from audit reports and publish & analyze	. 	\nRoles & Responsibility: 	 	\n   a. 	Responsible for text extraction using AWS	 Textract	. 	\n   b. 	Discussion with project planning team	 	\nTools/Technologies used:	 Python, NLP, A	mazon 	Machine 	Learning	, A	mazon	 SageMaker, AWS Textract, A	mazon	   	\n       	Comprehend	 	\n \nOPEN INSIGHTS	 PROJECTS	:  	 	 	 	 	 	 	Aug 2019 to Feb 2020	 	\n \nProject Title:	 BIFE 	- Banking Intension Fulfillment	 Engine (CLTV Module)	 	\nProject Profile:	 To predict customer lifetime value,	 the amount of revenues that you expect to generate	 from a 	  	\n  customer during the period over which	 your service will be of value. 	 	\nRoles & Responsibility:	 	\n    	a. 	Responsible for developing software.	 	\n    	b. 	Wrote 	down test cases for checking the	 functionality of various functions.	 	\nTools/Technologies used:	 Python, Machine 	Learning	 	\n \nTORCAI	 PROJECTS	:  	 	 	 	 	 	 	 	 	Feb 2019 to Jul 2019	 	\n \nProject Title:	 Zirca Campaign	 	\nProject Profile:	 To collect advertiser campaign	 information based on 	distinctive features	 performance to predict 	  	\n                         	and	 recommend	 blacklist & whitelist values.	 	\nRoles & Responsibility: 	 	\na. Responsible for collecting	 data 	using Spark 	and 	analysis	 data using keywords and 	based on	 result create 	\nblacklisted and whitelisted features	. 	\nTools/Technologies used	: Spark, Scala	 	\n \nFINDABILITY SCIENCES	 PROJECTS	: 	 	 	 	 	 	Jan 2018 to Jan 2019	 	\n \nProject Title:	 Ajinomoto Price prediction	 	\nProject Profile:	  To predict the price of product	 after every week, after 1 month, 	after 2 months and after 3 	  	\n                           	months	 value.	 	\nRoles & Responsibility: 	 	\na. Build the model using 	Linear, 	Random Forest	 regression 	and XGBoost regression.	 	\nb. Responsible for EDA, deliver insights and	 prediction result.	 	\nTools/Technologies used:	 R - Programming	, Machine 	Learning	 	\n \nProject Title:	 Net promotor score 	 	\nProject Profile:	 Classification of user sentiment for various telecom plans. 	 	\nRoles & Responsibility: 	 	\na. 	Build the model using 	Logistics regression, 	Random Forest	 classification	 and 	Naïve Bayes	 classification.	 	\nb. 	Responsible for predicting classification report.	 	\nc. 	Responsible for EDA, deliver insights and prediction result.	 	\nTools/Technologies used:	 R - Programming	, Machine Learning',2,2,1,'Candidate',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-25 13:09:42',49),(455,108,76,51,17,'76_108_51_28999818966a25e5e82b60_CV_Architect__Amol_Naik.pdf','CV_Architect__Amol_Naik.pdf','Resume of Amol	 	 	Page 	1 of 	9 	\n                    	         	           	             	       	               	\n 	\n 	\nAMOL	 HIRAMA	N NAIK	 	\nFLAT NO 	403	 BLDG	 TULIP 	ADITYA 	GARDEN CITY 	PHASE 	5  PUNE CITY 	PUNE 	(CB)	  411058	 	\n Mob: 	9356997358 	Email	: amolhiramannaik	@gmail	.com	 	\n \nEXECUTIVE 	PROFILE	. 	\n 	Around	 17 years of 	experience in	  Designing 	Web,	 Win	dow 	and Cloud Native 	Applications	. 	\n 	7+  years of  experience in  Solution architecture  and 	developing 	End  to  End 	complex  Single	-Page  Responsiv	e 	\nWeb Applications	 	\n 	7+ years of experience in designing, implementation a	nd architecting the cloud solution	-AWS,GCP,Azure	 	\n 	6+  years  of  experience  using  AWS,  ideally including  comfort  with  the  majority  of  the  following  services  (or \nequivalent):  EC2,  S3,  CloudFront,  Lambda,  IAM,  Route  53,  ECR,  ECS, 	EKS,	CodeBuild,  CodeDeploy, 	\nCodeP	ipeline, SNS, CloudWatch	,Datadog,AWS Neptune,AWS Control Tower	 	\n 	7+ years of rich experience in 	UI 	Architect	ing Scalable, Secure 	Web solutions	. 	\n 	9+ years 	of  experience  as  Full  stack  developer  in  building  web  App 	UI  Design, 	Micro  Frontends  using 	\nAngular2/4	/6	/8	/10	,AngularMaterial,	JavaScript	,HTML	5,ReactJS,	MongoDB,	Cassandra,	Microservices	,Spring	\nBoot,NestJS	,.Net Core 3.0	 	\n 	5+ years of client 	facing 	experience worked	 on 	cross	-functional	 teams for resolving various functional issues 	\nand gathering appropriate requirement	s.  	\n 	3+ years of working experience on Developing Window Applications using WPF with MVVM	 Pattern	 	\n 	Support	ed 	the  business  development  lifecycle  (Business  Development,  Capture,  Solution  Architect,  Pricing 	\nand Proposal Development)	 	\n 	Strong  knowledge  of  Amazon  K	inesis,  AWS  Lambda,  Amazon  Simple  Queue  Service  (Amazon  SQS), 	\nAmazon Simple Notification Service (A	mazon SNS),EC2,S	ssss	3,Bucket,ALB.	 	\n 	Strong knowledge with Web Services, API Gateways and application integration development and design	. 	\n 	 GCP Knowledge of Clou	d Function,Google Kubernetes Engine(GKE),Cloud Pub/Sub,Cloud Load Balancing	 	\n 	Skilled 	in Code Optimization, Review,	 Perfor	mance Tuning for web application	 and SQL	 server 	 Database	 	\n 	 Implementation  of  design  patterns  such  as  Singleton, 	Factory,  Abstract	 Factor	y,  Command	,  Adapter	,  	\nObserver 	in .Net	 	\n 	 Implementation of Software Design Principle, SOLID Principles	 	\n 	Worked on Dependency Injection for code refactoring and loose coupling	 	\n 	 Extensively used core 	.Net technologies like 	LINQ, SOAP Web Services, WCF	 	\n 	 Extensi	vely used Microsoft Tools like Enterprise Library, Entity Framework, etc	 	\n 	 Experience in writing Reporting 	Services	 Rendering, Delivery and Data Source Extensions	 	\n 	 Experience  in  providing  Proposals,  Estimations  (Task  Ba	sed  and  Function  Point)  for  New 	Develo	pment, 	 	\nReengineering and Sustained Engineering projects	 	\n 	 Well  versed  with  complete  Software  Development  Life  Cycle  (SDLC)  process  which  includes	 Design, 	\nConstruction, Testing and Deployment	 	\n 	 Created UI Library using	 Angular Material and Storybook	 componen	t Library.	 	\n 	Proficient 	knowledge in SSO,	 Saml,	 OpenIDConnect, OAuth 2.0	 	\n 	Experienced in working on RFP	 and 	Presales	 	\n 	Worked  with  DevOps  tools  Jenkins  for  Continuous  Integ	ration, 	Deployment,  JIRA	 for  project  Management 	\nand Confluence for 	keeping project	 relate	d documents.	 	\n 	Worked with testing tools framework Jasmine, Karma and Protractor	 	\n 	Worked with 	IIS and .Net Assemblies	 during application deployment.	 	\n 	Profound knowledge in working with 	RD	BMS includes MS SQL Server 2012	\\2008	, Oracle	11g	 	\n 	Excellent skills in writi	ng 	stored 	procedures, T	-SQL 	Queries, 	functions, triggers, views	 and schemas.	 	\n 	Good Experience with Unified Modeling Language(UML) standards and documentation and experience	 	\nIn preparing 	Class 	Diagrams, Sequence	 Diagrams using	 design tools 	such as Microsoft 	Visio.\n\nResume of Amol	 	 	Page 	2 of 	9 	\n 	Performed  Unit  Testing  using  tool  NUnit 	and	 performed  several  integration 	testing,  System	 testing  and 	\nperformance testing.	 	\n 	Strong  analytical  and  dynamic  trouble  shooting  skills coupled  with  excellent  communication  skills  and  able \nto work individuall	y and as a team.	 	\n 	Basic knowledge of Generative AI(Currently attending classes on Generative AI with Great Learning)	 	\nCERTIFICATION	 	\n 	\n 	TOGAF 9(Combined Level 1 & 2) Certified Professional	 	\n 	Google Professional Cloud Architect	 	\n 	AWS  Certified Solutions Architect	-Associate	 	\n 	Microsoft Certified: Azure Solution Architect Expert	 	\n 	SAFe 4.0 Agile Certified Professional	 	\n 	Microsoft Certified: Azure AI 900 Fundamentals	 	\n 	Generative AI for Business with MSFT Azure Open AI Program	 	\n 	\nORGANIZATIONAL EX	PERIENCE	 	\n6 Jan 2022 to till date	          	T-Systems as	 Senior Technical Architect.	 	\nApril 2021	 to 	4 Jan 2022	     	HCL Technologies 	as Senior Technical Architect	 	\nMay 2019 to 29 Jan 	2021 	   Western	 Union	 Technology 	Engineering as	 Solution Architect.	 	\nMay 17 to 	April 2019	       	  Infosys, Pune	 as Technology Architect	 	\nNov’11 	 - May 17	 	Tata 	Consultancy	 Services, Pune as Assistant Consultant	 	\nSep’10 	– Nov’11	  	Syntel Ltd., Pune as Analyst Programmer	 	\nJul’08 	– Aug’10	  	Acidaes Solutions Pvt. Ltd., Mumbai as Engineer 	- Application Development Group	 	\nJan’	08 	– Jul’08	 	 	Orion India Systems Pvt. Ltd., Mumbai as Software Developer	 	\nApr’07 	– Jan’08	  	IL & FS Education & Technolo	gy, Mumbai as Software Developer	 	\n \n \nCAREER 	FOCUS	 	\n 	Enterprise  application  |  Solution  Architect  |  Cloud  Migration  |Digital  Transformation  |  M	icroservices  | 	\nStrategic  Planning &  Execution  |  Leadership  & Team  Building  |Customer  Relationship  | Generative  AI  for \nBusiness	 	\nTECHNICAL 	SKILL 	SET 	\n \nProgramming Languages	 	C#,JAVA, Typescript	 	\nJavaScript  Framework  & \nLibrary	 	\nAngular	 14, NodeJS	,Javascript, Boo	tstrap	,ReactJS,Angular	Material,	 	\nStorybook UI Component 	library, Web Component	,SpringBoot	,NestJS	 	\nDatabase	 & No SQL DB	 	SQL Server 2012	\\2008	,Oracle11g	,MongoDB	,Casandara	 	\nMiddleware	 	Microservices	,Kafka	,RabbitMQ	,Springboot	,Python	,Micronaut	 	\nBuild  Packaging  Too	l 	and 	\nFramework	 	WIX 3.7	,MSBuild	,Angular CLI,Gulp	 	\nModeling 	 and Design Tools	 	UML,MS Visio	,Enterprise Architect	 	\nTestingTools Management and \nFramework	 	NUnit	,Jasmine,Karma,Protractor	,SonarQube,Resharper,HP ALM	 	\nVersion Control Tools	 	MS Visual Source Safe(VSS	),SVN,TFS	,Perforce,JIRA	,Github	 	\nDimension	 	\nCloud 	 Computing	 	\nAWS	EC2,RDS,S3,Glacier,SQS,SNS,VPC,CloudFormation,IAM,Route53	\n,EBS,Lambda,Kinesis,API  Gateway,ALB	 GCP	-Cloud  Function,Google 	\nKubernetes  Engine(GKE)  Cloud  Pub/Sub,Cloud  Load \nBalancing.	Azure	 Event Hubs,	API Management,Storage,Queues,	 	\nSecurity	,AWS Datadog,AWS Neptune,AWS Control Tower	 	\nOperating System	 	Win 95/98/NT/XP	 /Win 2003	/Win 2007	 	\nDevOps 	 	Jenkins, 	JIRA	, Docker	, Kubernetes	,OpenStack\n\nResume of Amol	 	 	Page 	3 of 	9 	\n \n \n \nPROJECTS 	EXECUTED 	 	 	\n \nT-Systems as Senior Technical Architect	 	\nDuration: January 2022 to Till Date	 	\nProject: LogisticBus: The LogisticBus is a system used 	Daimler	 for supplying parts of vehicles to Retailer.	 	\nLogisticBus  systems  consists  of  Modules  like  Dealer  Inventory  Management  Systems,  Stock  Information  and \nControll	ing 	Systems	. When	 retailer, systems, places the order	 checks if the part is availab	le with wholesaler.	 	\nAbout  90%  parts  ordered  by  retailer  are  available  with  corresponding  wholesaler.  This  is  possible  due  to \napplication such as systems demands and Forecast	 applications. But if it is not available with wholesaler then the 	\nrequest goes to central warehouse and retailer is informed after how many days his demand will be fulfilled	 	\n 	Responsible for delivering architecture guidelines for LogisticBus Modernization 	Application.	 	\n 	Le	d development team in 	Modernization	/Transformation 	LogisticBus	 legacy Application.	 	\n 	Involved 	in 	the 	requirement gathering m	eetings with Product owner. 	 	\n 	Created the Micro services Orchestration layer for 	retailer order, Transferring order deta	ils to other systems.	 	\n 	Worked with the UX designer in designing the wireframes like lending page with the main components.	 	\n 	Design, both technically compliant and secure Cloud solutions, as well as value	-based, on	-demand services to 	\nfacilitate the effective 	transition and migration of projects and p	rograms into 	unique 	cloud	 environment	. 	\n 	Design and implemented AWS Simple S	torage Services to store sales inventory information.	 	\n 	Worked with the EC2 instances to host the 	Micro services	 and the Application.	 	\n 	Involved	 in creating the VPC used cloud Formation Template	 	\n 	Used the Hystrix Library to implement the circuit pattern for 	Micro services	. 	\n 	Implemented the AWS API Gateway for 	Authorization, caching	 and storing the  REST API  Response.	 	\n 	Involved in exploring the cloud	 agnostic architecture AWS,Azure and GCP	 	\n 	Involved 	in Deploying	, Monitoring and Hosting of the 	Micro service	. 	\n 	Used Angular Material	 to design	 and build up a multi 	layered System	. 	\n 	Used Sonar Qube to 	monitor	 the Code Quality Metrices.	 	\n 	Involved in code review 	and approve the Pull Request	 	\n 	Led the deployment of  the code builds and helping in continuous integration and deployment.	 	\n 	Used JIRA, Remedy to create user stories ,is	sues, plan.	 	\n 	Involved in creating the Database Model	. 	\nEnvironment: 	Visual Studio Code, 	AWS 	Cloud.	Angular	 12, Typescript,HTML5, 	\nCSS,JIRA,,AngularC	LI,Angular Material PostgreSQL,	Kafka,Amazon Web 	\nservices,Docker,Gitlab,NodeJS,Java,SpringBoot	,Kubernetes,	IntellijIDEA,	 	\n \nHCL Technologies as Senior Technical Architect	 	\nDuration	 April 2021	 to January	 2022	 	\nProject:  Working on a project to 	enhance	 secured password system for a US based client	. 	\nEnvironment: 	C#	, Web	 Services	, WPF, SQL	 Server	 	\n \nWestern 	Union, Pune	 	\nDuration: 	May 2019	 to 29 Jan 2021	 	\nProject:	 iWatch	,RTRA,Transvision  applications  are  involved  in 	case  management  application  that	 provides 	\ncompliance officers, 	investigators and  analysts  the  ability  to  create 	and/or  review information  about	 a  subject  or 	\ngroup of subjects for the purposes	 of investigation, assessment, 	and intelligence maintenance. iWatch	 was crea	ted 	\nin response to requirements 	from the Global Compliance organization and is intended to serve	 as a single “source 	\nof truth” 	for compliance activities globally includin	g those compliance user groups.	An iWatch case can be for any 	\nsubject such as 	an Agent, a consumer, a geography or a group/network of Agents and/or Consumers.	 	\nRTRA is a Real Time Risk Analysis application provides the details about the 	customers	. 	\nTransvision  is  a  read  only  web 	application  used	 by  the  Agents  to  monitor 	the  suspicious	 activity  on  the 	\ntransactions.	 	\n \nResponsibilities:	 	\n 	Responsible 	for delivering architecture	 guidelines	 for iWatch Modernization Application.	 	\n 	Le	d development	 team in 	Modernization	/Transformation iWatch	 legacy Application.	 	\n 	Involved 	in 	the 	requirement gathering	 meetings with 	Product owner	.  	\n 	Created the 	Micro services Orchestration	 layer for Case creation, Case bulk load, Case push ,Case search, 	\nCase Integration with other application.	 	\n 	Worked with the UX designer in designing the wireframes like lending page with	 the main components	.\n\nResume of Amol	 	 	Page 	4 of 	9 	\n 	Design, both technically compliant and secure Cloud solutions, as well as value	-based, on	-demand services to 	\nfacilitate the effective transition and migration of projects and p	rograms into unique 	 cloud environment	. 	\n 	Design	 and impleme	nted AWS Simple Storage Services to store and retrieve user specific kyc document	 	\n 	Worked with the EC2 instances to host 	the Microservices	 and the Application.	 	\n 	Involved in creating the VPC used cloud Formation Template	 	\n 	Used the Hystrix Library to implement 	the circuit pattern for Microservices.	 	\n 	Implemented the AWS API Gateway for Authorization,caching and storing the  REST API  Response.	 	\n 	Involved in exploring the cloud 	agnostic architecture 	AWS	,Azure and GCP	 	\n 	Involved in  Deploying, Monitoring and Hosting of 	the Microservice.	 	\n 	Used Angular Material	 to  design and build up a multi layered  System.	 	\n 	Used Sonar Qube to monitored the Code Quality Metrices.	 	\n 	Involved in code review and approve the Pull Request	 	\n 	Led the deployment of  the code builds and helping in cont	inuous integration and deployment.	 	\n 	Used JIRA, Remedy to create user stories ,is	sues, plan	. 	\n 	Involved in creating the Database Model	 	\n 	Created and maintained the confluence page for all project specific details like Project Architecture etc	 	\n 	Involved in the PI 	planning to select the MBI and grooming the team on same.	 	\nEnvironment: 	Visual Studio Code, Angular	 8, Typescript,	HTML5, CSS,JIRA,,AngularC	LI,Angular Material 	\n,Cassandra,Kafka,Ama	zon Web s	ervices,Docker,Gitlab,NodeJS,	Java,	SpringBoot	,Kubernetes,AWS	 & 	\nGCP	,SSO	,ASP.Net core 3.0	 	\n 	\nInfosys Ltd,Pune	 	\nClient: E.ON UK	(Worked at client location  Nottingham,UK from June 18 to Feb 19)	 	\nDuration: May  2018 to April 2019.	 	\nGSOS	-Guaranteed Standards of Service: These are the standards set by the E.ON and some of these are part	 of the 	\nobligations  set  for  energy  companies  by  Ofgem,  the  energy  industry  regulator.  Some  are  standards  that  the \nelectricity  distributors  and  gas  transporters,  who  make  up  the  distribution  network,  have  to  meet.  If  these \nstandards  doesn\'t  meet  then  electr	icity  distributor  or  gas transporter 	- will  pay  to  the  customer  subject  to certain 	\nexemption	 	\nResponsibilities:	 	\n 	Led in requirement gathering meetings with business customers.	 	\n 	Technical design including developing the component views of system	 	\n 	Design/Develop	ment of a new .Net Core Microservice to read Servicing data that invoked by the API.	 	\n 	Created MSMQ  to capture the customer transactions and support the developers in implementation of \ndesign wireframes.	 	\n 	Created the service layer which helps all developers/	 feature packs to utilize the APIs t	o be consumed in app	 	\n 	Created standardized API designing such that every  supplier uses its own RES	Tful API design	 	\n 	Involved in  Deploying, Monitoring and Hosting of the Microservice.	 	\n 	Used Angular Material	 to  design and b	uild up a multi layered  System.	 	\n 	Led the deployment of  the code builds and helping in continuous integration and deployment.	 	\n 	Used JIRA, Remedy to create user stories ,issues, plan sprints and to distribute tasks amongst team members.	 	\n 	Worked on the Advance	d queue on high volume transactional systems in Oracle.	 	\n 	Involved in DB Performance Optimization as well.	 	\nEnvironment: 	Visual Studio Code, Angular	4/6	, HTML5, CSS,JIRA,,	NodeJS,AWS,	AngularCLI,Angular 	\nMaterial ,Oracle 11g.	Azure	,ASP.Net core 2.0	 	\nInfosys Ltd,Pun	e 	\nClient: HSBC	 	\nDuration: May 	 2017 to March 2018	 	\nStaff  Frond  End  :  The  SFE  is  an  web  application  used  by  the  front  end  bank  employees  to  get/view  all  the \ncustomer related information like Customer overview, Customer Family ,Loan Repayment schedule and Loan	 	\nRepayment Holiday etc. 	 	\nResponsibilities:	 	\n 	Development and technology planni	ng and implementation 	– providing	 guidance in best practice and tool 	\nuse in the design and build of technical frameworks and the applications built on them and own and \nimplement co	mplex area of the UI systems.	 	\n 	Architecture and design 	- proposes & implements technical solutions, ensures their integrity, and that the 	\nsolutions can be built, implemented and maintained within timelines/budgets.	 	\n 	Technical design authority 	- internally re	cognized as a specialist in complex technical areas, will oversee 	\nbenchmarking, installation, upgrade, configuration, deployment, and testing activity.\n\nResume of Amol	 	 	Page 	5 of 	9 	\n 	Improves quality 	- investigates and prototypes innovative approaches to improve software quality.	Provide	 	\nStability 	- ensures development teams can develop in a stable and productive environment, with minimal 	\nrework required due to architectural change or related problems as products evolve.	 	\n 	Assisting Project Management in planning technical aspects of projec	ts. 	\n 	Providing regular updates on project status and risks.	 	\n 	Supporting and mentoring individual developers and ensure the team is positive & motivated.	 	\n 	Performing detailed technical reviews to ensure the overall design is being implemented.	 	\n 	Full stack	 Web F	ramework development experience.	 	\n 	Demonstrable detailed understanding of architecture principles and methods, technology, and standards.	 	\n 	Utilizing 	research	 and self	-learning to ensure technical credibility.	 	\n 	Work	ing	 with UI Designer to provide feasibility di	rections and lead UI development team.	 	\n 	Possess	ing 	good	 analytical, communication & interpersonal skill.	 	\n 	Hands on experience in responsive designs  (Bootstrap, Telerik or any other framework)	 	\n 	Participated in meetings with Business users and with other proje	ct means to ensure that the volatile	 	\n business requirements reflecting the project dev	elopment.	 	\n 	Contributing 	to the implementation of User Stories	 	\n 	Participating 	in SCRUM process	 	\n 	Fulfilling 	the acceptance criteria with the contributed code 	(quality, unit te	sts e	tc.)	 	\n 	Adherence to architecture, design and quality guidelines	 	\n 	Maintenance, problem analysis and bug fixing within the 	application	 	\n 	Technical clarifications with other team members	 	\nEnvirionment: 	Visual 	Studio Code, 	\nAngular	,HTML5,CSS,GitHub	,JIRA	,Jenkins,	Protractor,	NodeJS,AWS,	AngularCLI	,OAuth2.0	, ,ASP.Net core 2.0	 	\n 	\nTata Consultancy Services,	 Pune	 	\nClient:NBS Bank	 	\nDuration	:Nov  2015 to March 2017	(Worked at client location Swindon,UK	 from 	Nov 2015 to Nov 2016	) 	\nThe  Nucleus  is  a  web  application that  will  enable	 the  user  to  carry out  account  and customer  related  back  office 	\nservices on the accounts stored in SAP.Nucleus consists of a number of back office services which can be invoked  \neither  from  the  Nucleus  application  or  externally	 from  other  applications  by  p	assing  appropriate  data  to 	\nNucleus.The  external  applications  can  either  invoke  the  Nucleus  application  as  a  whole  or  a  specific  Nucleus \nprocess.In  both  the  cases  the  external  application  needs  to  pass  appropriate  data  confirming  to    a  pre 	defined 	\ninterface	.Nucleus  also  contains  links  to  other  many 	applications.  The	 data  that  is  available  within  Nucleus	 is 	\npassed to 	these external systems	 using the interface.	 	\nResponsibilities:	 	\n 	Develops software solutions by studying information needs; conferring with users; 	Studying systems flow, 	\ndata usage, and work processes; investigating problem areas; Following the software development lifecycle.	 	\n 	Develop features across multiple subsystems within our applications, including collaboration in requirements \ndefinition, proto	typing, design, coding, testing and deployment.	 	\n 	Documents and demonstrates solutions by developing documentation (HLD, LLD), flowcharts, layouts, \nssss	diagrams, charts, code comments and clear code.	 	\n 	Design,  develop  and  maintain  the  Database  Schema,  Entity  r	elationship  diagrams,  Data  modeling,  Tables, 	\nStored procedures, Functions and Triggers, Constraints, Indexes, Schemas, Functions, Views, Rules, Defaults \nand complex SQL statements. 	 	\n 	Maintain a technical understanding of a component or sub	-component within 	an application / product, 	\nproviding support for technical queries, live incidents and problems. 	 	\n 	To ensure that the technical integrity and quality of the applications / products is maintained across their \nlifetime, contributing to the development of any t	echnical changes being proposed. 	 	\n 	To build knowledge of how technical strategies are defined to provide business solutions.	 	\n 	Review Technical Feasibility Studies for projects that impact applications / products within domain, and \nassess potential impact.	 	\n 	So	lve software	-related problems, providing analysis and recommendations as appropriate. 	 	\n 	Develop an awareness of new tools, processes and technical developments relevant to component area of \nexpertise.	 	\n 	Understand emerging software development methods, tools 	and standards. Look for ways to apply as 	\nappropriate	 	\n 	Provide input to end	-to-end system technical designs that involve applications / products within domain, 	\ncollaborating with other internal and supplier teams as required.	 	\n 	Understand the various phases of	 the software development lifecycle.	 	\n 	Undertake impact assessment of change requests against applications / products within domain.\n\nResume of Amol	 	 	Page 	6 of 	9 	\n 	Review test strategies to guarantee the quality of the delivered software assist in the diagnosis of system \nproblems encounte	red during testing. 	 	\n 	Collaborate with other members across teams to build technical solution. 	 	\n 	Define architecture, R&D on new technologies	 	\n 	Make the framework up	-to date based on new technology or new way to implement the software solutions.	 	\n 	Documenting st	andard features, guide to integrate framework features, user manuals, versions etc.	 	\n 	Create deliverables (Packages, Configurations) using IT Strategy approved build tools (Team foundation \nservers) and standards, ensuring they meet all specific install polic	ies defined for the system.	 	\n 	Maintained the source	-code repository, developed multi	-platform product installers and developed scripts 	\nfor builds and source code automation.	 	\n 	Co	-ordination with the Technical Project Managers, Software Developers, Testers, App	lication Support, SAs 	\nand DBAs for any Deployment and Change across environments.	 	\n 	Developed and managed testing and roll	-out project plans for software releases, desktop updates and server 	\nimplementations.	 	\n 	Designing and creating the web services using WCF 	and SQL Server 2010.	 	\n 	Designing the application by using the different design pattern like creation pattern (Factory design pattern, \nMVC, Abstract Factory, and Singleton) and structural pattern (Adapter, Facade etc.).	 	\n 	Complete assignment work to budget, tim	e and quality requirements. Document all work in accordance with 	\nagreed standards, and with re	-use in mind	 	\n 	Involved in Project planning, Resource utilization & Timeline estimations.	 	\n 	Estimating the tasks based on functional requirement and assign the work t	o team	 	\n 	Managed the  offshore and onshore team.	 	\nEnvirionment: 	C#,Visual Studio 2015	, WCF,	SQL Server,,AngularJS,NodeJS	,SAML	 	\n 	\nTata Consultancy Services,Pune	 	\n Client:DNB Bank	 	\nDuration	:Jan2014 to Oct 2015	 	 	\nDescription:	 DLA  (DNB  Loan  Administration)  application  i	s  used  for  Administration  of  Currency  Loan  as 	\nwell  as  tailor  made  loans  in  NOK.  DLA  is  applicable  for  large  corporate 	customer 	and  limited  group  of 	\nprivate customers (Multi Currency Loans only).	 	\n 	Zenit  Workflow  interacts  with  different  rule  engines  and  syst	ems    and  the  offer  letter  to  the  customer   	\ngenerated for the customer mentioning limit and terms and condition. DLA loan is offered to very limited \nspecial customers only, so most of the terms and condition negotiated between loan officer and customer \nout	side the system over the counter.	 	\n 	Large  Corporate:  For  large  corporate  &  International  Customer,  ASAP!LC  takes  care  of  the  Credit \nProcess.  As  the large  corporate  customers involve  high value  loans,  most  of  the  agreement  has  been  set \noutside the box between	 the lawyers of customer and DNB. Details agreement prepared based on various 	\nnegotiation factors like limit, collateral, margins etc. A Credit process can be initiated in ASAP!LC for an \nexisting customer or may be for a new customer.	 	\n 	After  Credit  process 	done  and  finalization  of  the  agreement,  A  Decision  have  been  have  been  taken  to 	\ngrant  the  loan  and  Limit  have  been  decided.  After  negotiation  of  agreement  have  been  over,  a  request \nhave been sent to DLA users through mail to open a loan project in the DLA 	system.	 	\n 	For  a  DLA  Loan collateral is  being  saved  to  Felix  Depot by  the  collateral  department  and  the  Collateral \nDepartment gives the final sign off and send the agreement to DLA department for further processing.	 	\n 	After  receiving  the  credit  decision,  DLA  Ap	plication  users  open  a  new  DLA  loan  project  using  the 	\nCustomer details provided in the agreement. 	 	 	 	 	\n 	After  Credit  process  done  and  finalization  of  the  agreement,  A  Decision  have  been  have  been  taken  to \ngrant  the  loan  and  Limit  have  been  decided.  After  neg	otiation  of  agreement  have  been  over,  a  request 	\nhave been sent to DLA users through mail to open a loan project in the DLA system.	 	\n 	For  a  DLA  Loan collateral is  being  saved  to  Felix  Depot by  the  collateral  department  and  the  Collateral \nDepartment gives the 	final sign off and send the agreement to DLA department for further processing.	 	\n 	After  receiving  the  credit  decision,  DLA  Application  users  open  a  new  DLA  loan  project  using  the \nCustomer details provided in the agreement. 	 	\n 	From DLA client after creation  of 	the DLA  project  there  is  a limit  have  been  entered  for  customer  based 	\non  the  limit  set  in  the  Credit  Process.  This  DLA  limit  is  used  for  reference  and  warning  purpose  only, \nDLA limit will be changed manually based on revision of the limit from Credit Depar	tment	 	\n 	After creation project a Single Loan or Limit	-Tranche Facility have been created through DLA client and 	\nthe amount getting disbursed as per the agreement.	 	\n 	After creation of project and loan it sends information to Felix exposure.	 	\n 	Loan  disbursement  is	 a  manual  process  in  DLA.  DLA  user  need  to  disburse  the  loan  to  the  customer 	\naccount. In case of non DNB customer Disbursement will be done through a internal Global Account.\n\nResume of Amol	 	 	Page 	7 of 	9 	\nResposibilities:	 	\n 	Participated in meetings with Business users and with other pro	ject means to ensure that the volatile	 	\n      	business requirements reflecting the project 	development. Application	 development was developed was 	 	\n      	based on UML methodology.	 	\n 	Involved in the design of the Use Case Diagrams and class diagrams using Micro	soft Visio.	 	\n 	Reworked on a part of the GUI to improve interface without affecting the performance of the application.	 	\n 	Used C# as language to develop code behind business logic	 	\n 	Used C# for middle tier business logic and implementation of Web Services	 	\n 	Followe	d the MVVM design 	pattern, separating	 the 	business, presentation	 and data models.	 	\n 	Performed the conversions of existing VB Applications into WPF application.	 	\n 	Designed the WPF GUI for accessing product 	details, Comparisons	 and Client details.	 	\n 	Developed User	 Interface(UI) using WPF for the better look and feel of the Window based application.	 	\n 	Wrote Stored 	Procedures, Triggers	 and SQL Queries and LINQ in SQL Server 2008.	 	\n 	Developed and consumed WCF Web Service to access data layer components	 	\n 	Involved in Impleme	ntation of Exception handling on User Interface layer.	 	\n 	Created test cases and did basic manual testing.	 	\n 	Used TFS as a central repository and source code control.	 	\n 	Mapping the business requirement to the technical specification and detail design documents.	 	\n. 	\nEnvirionment: 	C#,Visual Studi	o 2012	, WCF,WPF,S	QL Server,Entity 	Framework., Angular	 JS,MVC.	 	\nTata Consultancy Services,Singapore	 	\nClient:Barclays Bank ,Singapore	 	\nDuration	: April 2013 to  Dec 2013.	 	\n Barclays is a major global financial services provider engag	ed in retail and commercial banking, credit cards, 	\ninvestment banking, wealth management and	 investment management services	 	\nDescription:Exposure Analysis & Reporting	-Credit Risk Portal	 	\nExposure, Analysis and Reporting application in Credit Risk Portal is a	n application that supports and 	\nenhances new BASEL 	– III and SRA requirements. Changes are been introduced in new BASEL III and 	\nStrategic Risk Architecture (SRA) framework, which impact the current reporting architecture. This project \naims to build a syste	m that enable users to perform What	-if calculations and view pre	-calculated exposure 	\nmeasures, apply adjustments for any counterparty, site, product, what	-if trades, actual trades. View 	\nnumerical and graphical display of risk measures. View list of actual 	trades, trade attributes, settlement 	\nprofiles, netting agreements, Collateral arrangements etc for any CSP and Trades.	 	\nPortfolio simulations are required to generate risk measures to manage the counterparty and trading \nbusiness.This can be used to see the 	impact on a portfolio when new derivative and Securities Financing 	\ntrades are entered into by calculating several credit risk measures.	 	\nThe impact on the regulatory capital is also measured by calculating several regulatory metrics.	 	\nResponsibilities:	 	\n 	Respo	nsible for Design and Development of tactical IT solutions for Credit Risk Management System, 	\nactively involved in UI Design, WCF Services, Database Access Layer including Stored Procedures, \nCreating and maintaining Tables, Triggers, Views, Functions, Inde	xes for performance tuning. 	\nInvolvement in SRA Batches and Downstream Feeds for data to be picked up.	 	\n 	Using WPF, WCF, C#.Net 4, Visual Studio 2010, SQL Server 2008, Infragistics Controls, Xceed Grids and \nChart Controls to design and build up a multi layere	d Credit Risk System.	 	\n 	Championed using MVVM and PRISM design approach to ease separation of concerns between UX \ndesign, business logic, service back end and testing layers.	 	\n 	Involved in Performance Optimization at application and database level, Defect Fixe	s, Migration of 	\nExcel to Application and vice versa.	 	\n 	Involved in all aspects of design and development including co	-ordination of testers, BAs and business 	\nusers. Worked with business analysts to determine business rules and feature list.	 	\nEnvirionment: 	C#,	Visual Studio 2010, WCF,WPF,SQL Server,	 Multithreading	 and IIS.	 	\n Syntel. Ltd , Pune	 	\nAMEX 	 	\nClient:American Express 	 	\nDuration:	 Sept 2010  to 18 Nov 2011	 	\nRole:	 Analyst Programmer	 	\nDescription	: 	\n 	Statement  of  Work  (SOW)  Module:Users  can  create  a  request,  fill  out 	the  forms  by  selecting 	\nappropriate  request  type  and  submit  that  SOW  request,  which  notifies  respective  teams  to  take \naction  Once the request process is completed, the status will  be changed to complete      	 	\nand request can be closed. However, request wi	ll be in DB for future reference	.\n\nResume of Amol	 	 	Page 	8 of 	9 	\n 	Resource  Change  Request:Change  can  be  made  for  changing  the  request  by  leader,Requestor  only	 	\ncancel the request.After complete approvation of request,resource allocates to the requestor.	 	\n 	Work  In  Progress(WIP):This  module  i	s  used  to  manage  Project  Estimation  where  transfer  of  hours 	\nfrom projects is managed.	 	\n \n        	Responsibilities:	 	\n 	Involved  in  Requirement  Analysis,designed  and  developed  Web  application  using  C#.Net  4.0  and \nSilverlight.	 	\n 	Gathering the Client Requirements by 	participating in minute	-to-minute meetings and Analysis.	 	\n 	Design the High Level and Low Level Design of the system.	 	\n 	Programming Using Silverlight,C#	 	\n 	Developed  UI  Using  Silverlight  Controls  and  implemented  business  components  and  data  access \ncode using C#.	 	\n 	Involved in the database design and design tables,indexes and views.	 	\n 	Developed Tables,Indexes,Stored Procedures,Database Triggers and Functions.	 	\n 	Implemented WCF Web Services for database connectivity in Silverlight application.	 	\n 	Analyzed and formulated the T	est plan for requirements,created and test scenario from use cases,	 	\ntest case design from design document,regression and test automation using NUnit.	 	\n 	Involved  in  Unit  and  module  level  testing,reviewing  the  requirements  and  fixing  the  defects \nappropriately.	 	\n 	 Involved in detailed documenting of the application that was developed by me.	 	\n 	Provided production support and deployment support .	 	\n 	Project version management and code control and management was done in VSS.	 	\n 	Project was delievered following CMM methodolog	y. 	\n        	Envirionment:	 C#,Visual Studio.Net,Silverlight,WCF,WPF,SQL Server,Nunit and IIS.	 	\n Acidaes Solutions Pvt. Ltd ,Mumbai	 	\nCRMnext	 	\nClient	: HDFC Bank	 	\nDuration:	 Jul 2008 to Aug 2010	 	\nRole:	 Engineer 	- Application Development Group.	 	\nDescription	:  CRMnext  is 	a  business  application  designed  specifically  to  address  companies  wide  ranging 	\nneeds  of  managing  collaboration  and  interactions  between  vendors,  suppliers,  and  customers.  It  helps \ncompanies  efficiently  manage  the  front	-end  processes,  provide  consistency  ac	ross  all  customer  interaction 	\npoints,  and  focus  on  efficiencies,  boosting  bottom  line  revenue  potential.  Implementing  CRMnext  is  a \nstrategic  initiative  for  any  company.  CRMnext  provides  the  technology  platform  required  to  implement  an \nenterprise  CRM    initi	ative.    A  typical  CRM  initiative  is  a  mix  of  strategy,  process  and  technology  CRMnext 	\nenables companies to better manage and seamlessly share information, across departments by centralizing all \ncustomer  data.  It  automates  various  processes  using  its  workfl	ow  capabilities,  provides  powerful  reporting 	\nand  analytical  capabilities  that  deliver  actionable  business  intelligence.  This  enables  CRMnext  to  help \norganizations  create  a  true  360°  view  of  their  own  customers  that  spans  across  departments.  Typically,  an \nend	-to-end  CRMnext  deployment  will  cover  all  customers  facing  departments  and  manage  the  entire 	\ncustomer  lifecycle.  It  covers  the  entire  working  of  the  company  starting  from  lead  generation,  to  closure  of \nsales and from customer service to cross	-sell / up	-sell services. 	 	\n        	Responsibilities:	 	\n 	Involved  in  defining  business  and  system  requirements  and  designed  the  functional  and  technical \nspecifications.	 	\n 	Programming using ASP.Net,C#.	 	\n 	Design and Developed web Forms using ASP.Net,XML.	 	\n 	Used ASP.Net Server Con	trols and HTML controls to design and web pages.	 	\n 	Involved in writing the programming code for web pages using ASP.NET object model.	 	\n 	Used ADO.Net Object Model to Interact with SQL Server Database using C# language.	 	\n 	Used Dataset object and Data Reader object	 to get the data from Database.	 	\n 	Wrote stored procedures and triggers.	 	\n 	Developed  Reporting  and  Analysis  module  by  web  enabling  reports,Web  development  using \nHTML,ASP.Net to deploy reports for the end user to view,print and export data from analysis purposes	. 	\n 	Involved in the development of the test plan Visual Source safe used on source control tool.	 	\n 	\nEnvironment:	 C#, ASP.Net, SQL Server 2005,Javascript,WCF,AJAX  Nunit,Silverlight	 	\n Orion India Systems Pvt. Ltd., Mumbai	 	\nVector 2008	 	\nClient:	 KPMG\n\nResume of Amol	 	 	Page 	9 of 	9 	\nDuration:	 Jan 20	08 to Jul 2008	 	\nRole:	 Software Developer	 	\nPlatform: 	Windows 2003	 	\nDescription	:   This is a custom auditing tool developed for the pioneers in auditing having their footprint all 	\nover the world. The application is developed in .Net  with client component devel	oped in ASP.Net with a 	\nservice running on the server and backend being MS SQL server.  The application is designed to manage all \nthe activities involved in the auditing process. It also uses service oriented architecture for the integration of \nplug in tool	s which allows system integration through loosely coupled components. The system has also got a 	\ncustom made document management system incorporated to it which holds the information in the SQL file \nsystem.	 	\nResponsibilities	   	\n 	Developed web forms using C#,AS	P.Net and SQL Server 2005.	 	\n 	Used Data Binding to bind Servers controls with the parameter from the tables in the database.	 	\n 	Published  Web  Services  using  C#  to  interact  with  the  other  applications  and  exposed  them  using SOAP \nand HTTP.	 	\n 	Designed and Developed A	SP.Net pages including Master and Navigation pages.	 	\n 	Implemented Data Grid view adding,deleting,sorting of rows for all the contacts and addresses of clients.	 	\n 	Involved  in  unit  and  module  level  testing,reviewing  the  requirements  and  fixing  the  defects \napprop	riately.	 	\nEnvironment:	 C#, ASP.Net, SQL Server 2005,Javascript,WCF,AJAX  Nunit.	 	\n 	\n IL & FS Education & Technology, Mumbai	  	\n Assessment Online	 	\nClient:	 ABNAMRO Bank, ICICI Prudential	 	\nDuration: 	April 2007 to Jan 2008	 	\nRole: 	Software Developer.	 	\nDescription	: Assess	ment Online is an online testing engine, which facilitates: 	 	\nCreation of Tests	 - Tests in any domain can be created from a global question bank (owned by organization)	 	\nAdministration of Tests	 - The tests can administer on	-line question bank or any personal	 question bank of a large 	\nof employer 	.The System enables frequent and Effective evaluation of employee and provides feedback about the 	\nTest  to  the  test  administrators  in  terms  of  statistical  analysis  of  questions  and  tests.  The  Assessment  Online  is  a \npowe	rful  Application  suite  of  creating  tests  and  surveys  on  the  web.  AOL  is  designed  to  deliver  online 	\nassessments  that  can  test  not  only  standard  literacy  and  numeric  skills  but  also  avows  organizations  to  easily \ndesign  their  own  questions.  With  AOL  software,	 you  can  quickly  and  easily  create  and  administer  online  tests 	\nwith minimal effort, giving you more time to spend with learners in the classroom or the workplace. 	 	\nResponsibilities:	  	\n 	Developed and Implemented the web page interfaces	 	\n 	Created  ASP.Net  pages  u	sing  C#.Net  for  middle	-tier  processing  of  clients  requests  of  clients  requests 	\nwith ADO.NET interfaces for database and implemented stored procedures.	 	\n 	Used  ADO.NET  and  data  objects  such  as  Data  Adapter,Data  Reader,Dataset,data  table  for  consistent \naccess t	o SQL data sources.	 	\n 	Used SQL Server as RDBMS to create and maintain Tables,Procedures and Views.	 	\n 	Implemented session state management	 	\n 	Migrated the functionality from existing asp pages to  new aspx pages and also added new functionality \nto reflect the Busin	ess users requirements.	 	\nEnvironment:	 C#, ASP.Net, SQL Server 2005,Javascript,Web Services.	 	\n \nEDUCATIONAL 	QUALIFICATIONS	 	\n P.G.D.A.C (Software)	, Y.C.P.A.I.T, A.C.T.S, C.D.A.C Centre, Mumbai,(Feb,2007)	 	\n B.E (	Computer Technology	), K.I.T.S Ramtek, Nagpur,	 (June,20	02)	 	\n 	\nPERSONAL 	DETAILS	 	\n 	Date of Birth	: 1st January 1978	 	\n 	Languages Known	: English, Hindi and Marathi	 	\n 	Passport No:	 W7633055	 	\n 	Marital Status:	 Married',2,2,1,'Candidate',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-25 14:17:22',51),(456,109,73,46,NULL,'73_109_177263729066a60c6e71d77_Tabassum_Sayyad-HMI_Developer.docx','Tabassum Sayyad-HMI Developer.docx','\r\nTABASSUM\r\nRESUME\r\n617855199279617855319294Experience Summary and Skill Set\r\nExperience Summary and Skill Set\r\ntabassum.sayyad1@gmail.comContact No: +91-9834112268\r\nResult oriented professional with 8.6 years of versatile experience in Android, Kotlin and Java technology.\r\nCurrently functioning as a Lead product Engineer with Harman.\r\nWorking on Android Automotive domain HMI, DCSM, Android Auto, CarPlay.\r\nWorked on AOSP code.\r\nInvolved pro-actively in various phases of Software Development Life Cycle in terms Analysis, Design, Development, Testing, Implementation, and Maintenance &amp;amp; Support.\r\nProficient experience in developing applications using Android using Java, Kotlin, React Native.\r\nHighly dedicated to produce professional work with highest quality and creativity.\r\nCommitment, hardworking, can grasp things quickly and think strategically.\r\nSUMMARY:\r\nGood knowledge of Android Framework API and Core Java and Kotlin.\r\nKnowledge of Design Patterns(MVP,MVVM), UI Material design(Recycler View, Coordinate layout and Constraint layout)\r\nExperience on remote data via REST and SOAP\r\nUnderstanding of JSON,XML parsing\r\nExperience in payment gateway\r\nExperience in third party libraries Retrofit, Volley, Picasso and Glide\r\nExcellent analytical and logical programming skills with a good understanding at the conceptual level\r\nGood in problem solving, communication skills and good team player\r\nAbility to learn new technologies and work in a dynamic environment and under pressure situations.\r\nHave working experience in agile and waterfall development process.\r\nExperience in Git and have basic knowledge of Jenkins configuration management tools.\r\nExperience to publish Apk on Google play store.\r\nComprehensive knowledge of React Native platform and core principle\r\nCommand over Kotlin.\r\nUsed Live Data\r\nImplemented Kotlin Coroutines for Thread Management.\r\nWorked using Room DB\r\nUsed data classes, Extension function\r\nWorked on RxjavaWorked on Jetpack component\r\nCOMPUTER SKILLS:\r\nLanguage/Platform: Kotlin, Core Java, Android, React Native, JavaScript, Apex\r\nDatabase: SQLite, Room Database, MySQL.\r\nOperating System: Win 10, Microsoft Win 7, Win 8, Ubuntu, Harmony OS\r\nEnvironment /Tools: Eclipse, Android Studio,STS, Android SDK, Git,Gerrit, BitBucketGitLab(Github),BugZilla, JIRA, Jenkins, SonarQube,VS code\r\n\r\nHarman. &ndash; BangaloreMay 2023 &ndash; Present\r\n    Project: PSA \r\n Client: Stellantis Description: The Android Automotive application for car brand called Peugeot, where car driver can   control climate, connect to mirroring projection like Android Auto or Car Play via Bluetooth or USB, able to play music by connecting to USB or Bluetooth, by projection user can connect voice call like incoming or outgoing call.\r\nTechnology: Android SDK, Java\r\n    Role:\r\n  Some of the key responsibilities while executing this project are as listed below -\r\nImplementation of features.\r\nFixing Framework Defects.\r\nWipro Ltd. &ndash; Pune, MHOCT 2020 &ndash; May 2023\r\nProject: ZRPL\r\nDescription: This application provides Wipro employees with substantial savings on ecommerce products. Employees can use this app to log in using their Wipro credentials and buy the product. This app provides a variety of brands, many of which provide substantial discounts on premium brands.\r\nTechnology: Android SDK, Kotlin, React Native\r\n    Role:\r\nPrepare solution based on understanding of the challenges.\r\nImplementation of the solution\r\nWorked in different module such as the product management, Address management and the Cart Module\r\n    Project: HR Direct\r\nDescription: The employee HR capabilities are accessed using this application. Once logged in, the user has access to timesheet, last six months&#039; worth of salary slips, and leave management. Here, employees can use the app to access their leaves and request leaves. Additionally, this application allows you to view and download an employee&#039;s pay slip.\r\nTechnology: Android SDK, Kotlin, React native\r\n    Role:\r\nUnderstand the various challenges faced by employee, management and HR and document the challenges in detail.\r\nDesign and development of the solution\r\nImplementation of the solution\r\nDelivered the project with 0 post-delivery defect\r\nTech Mahindra &ndash; Pune, MHAug 2019 &ndash; Oct 2020\r\n    Project: Messaging App\r\n   Client: HMD Global\r\nDescription: This application offers its users chat (RCS) and texting (MMS, SMS). Everyone has access to group texts and the ability to share images, GIFs, Emojis, videos, and voice communications.\r\nTechnology: Android SDK, Java\r\n     Role:\r\nDesign and development of the solution.\r\nImplementation of the solution.\r\nDevelopment of user friendly UI/UX added additional features like auto deletion of messages, auto texting at scheduled time and other features.\r\nBlue Planet Info Solutions (India) Pvt. LtdJan 2016 &ndash; Aug 2019\r\n \r\nWorked on various products across multiple domains. Product were developed using Android, React native and Java. Below is the list of products developed.\r\nSmartCookie Application which is a student teacher reward system\r\nOne Bay TouchPill Box\r\nTravel PillBoxVirtual PillBoxiTake-med\r\nSafety on Wheels\r\nKnow Your Medi\r\nEducation\r\nAmrutvahini College of Engineering, SangamnerBE in Information Technology | 2012-2015 | Percentage : 60%\r\nPVVP College of Engineering, LoniDiploma in Information Technology | 2009-2012 | Percentage : 67.25%\r\nSharda Kanya Vidya Mandir, RahataSecondary (12th Standard) | 2002-2003 | Percentage : 45.00%\r\nChandak Kanya Vidya Mandir, SinnarSecondary (10th Standard) | 2000-2001 | Percentage : 54.00%\r\nPersonal Details\r\nFull Name:Tabassum Abdul Latif Sayyad\r\nDate Of Birth:22nd May 1986\r\nGender:Female\r\nNationality:Indian\r\nLanguages Known:English, Hindi and Marathi\r\nAddress:Rabiya Nagar, behind Shri Hari hospital, Rahata 423107, Maharashtra.\r\nDeclaration\r\nI hereby declare that above mentioned information is genuine and true to the best of my knowledge.\r\nDate:/  / 2024Sincerely\r\nPlace:  Rahata(Tabassum Sayyad)\r\n',2,2,1,'Recruiter','SL','N',14,1,'2024-07-31 09:24:52','2024-07-28 09:24:52',0,NULL,NULL,'2024-07-28 09:16:30',0),(457,110,77,NULL,NULL,'77_110_159672912366a6566ea6855_Ujjwal_Mahar_26062024.pdf','Ujjwal Mahar 26062024.pdf','Ujjw al M ah arCloud and DevOps: 	Azure, AWS, Docker, Kubernetes, Jenkins, Github Actions, Prometheus, Grafana, Terraform	\nProgramming:	 GoLang, Python	 	\n&bull; D esig ned  a n d  d ep lo ye d  A W S in fra str u ctu re ,  in clu d in g  s e ttin g  u p  p ip elin es,  b acke n d  s e rv ic e s,  a n d\nm onit o rin g  s o lu tio ns f o r v a rio us c lie n ts .\n &bull; I m ple m en te d  c o st o ptim iz a tio n s tr a te g ie s t o  m axim iz e  e ffic ie n cy a n d  r e d uce  o pera tio nal  e xp en se s.\n&bull; M an ag ed  m ajo r p ro je cts  u tiliz in g  A W S s e rv ic e s s u ch  a s E C S,  E K S,  a n d  o th er D evO ps t o ols .\n&bull; E nsu re d  s c a la b le  a n d  r e lia b le  a rc h it e ctu re s t o  m eet c lie n t r e q uir e m en ts  a n d  b usin ess o bje ctiv e s.\n&bull; C ontr ib ute d  t o  G SoC ,  f o cu sin g  o n u se r-p ro vid ed ,  lo ng -liv e d  e n vir o nm en ta l  v a ria b le s in  U nik ra ft b uild s.\n&bull; I m pro ve d  K ra ft C LI t o  d yn am ic a lly  in je ct c u sto m  v a ria b le s,  in clu d in g  G CC v e rs io ns a n d  c o m pile -t im e s e ttin g s.\n&bull; E nab le d  f le xib le  a n d  c u sto m iz a b le  U nik ra ft b uild s f o r d eve lo pers .\n+91 7500070465| ujjwal.mahar01@gmail.com	  | Github	  | LinkedIn	\nE X P ER IE N CEDevO ps E ng in eer\nC lo ud Z en ia\nG oog le  S um mer o f C ode\nU nik ra ft\nN ove m ber 2 023 &ndash;  P re se n t	 	A pril  2 024 &ndash;  P re se n tE D U CA TIO NShard a U niv e rs it y  	B .T ech . in  C om pute r S cie n ce ,	\nIn d ia ,  G au ta m  B ud dha N ag ar,  G re a te r N oid a,  U P\nJun e 2 021 &ndash;  J un e 2 025	\nC G PA	:  8 .4	T E C H NIC A L	  S K IL LS	AWS Solutions Architect Associate	 	C ER TIF IC A TIO N SCours e ra  C ertif ic a te sK odeK lo ud	 	O ra cle  a ca d em yAW S	 	&bull; U tiliz e d  T erra fo rm  t o  b uild  a n d  c o nfig ure  A W S in fra str u ctu re ,  in clu d in g  E C S.\n&bull; I m ple m en te d  G it H ub  A ctio ns f o r C I/ C D  t o  a u to m ate  d ep lo ym en ts  u p o n c o de p ush .\n&bull; A ch ie ve d  s tr e a m lin ed  a n d  e ffic ie n t d ep lo ym en t p ro ce sse s f o r t h e A W S e n vir o nm en t.\n&bull;  I m ple m en te d  P ro m eth eu s f o r m etr ic s c o lle ctio n a n d  G ra fa n a f o r v is u aliz a tio n a n d  a le rtin g .\n&bull; A uto m ate d  A W S A uto  S ca lin g  b ase d  o n P ro m eth eu s m etr ic s f o r o ptim al  r e so urc e  u sa g e.\nP RO JE C TSAuto m ate d  A W S I n fr a str u ctu re  P ro vis io nin g  a n d  C I/ C D P ip elin e I m ple m en ta tio n	 	\nD evO ps P ro je ct\n C lo ud -N ativ e  A pplic a tio n M onit o rin g  a n d  O ptim iz a tio n\nD evO ps P ro je ct\n&bull; L ed  a ctiv it ie s a n d  in it ia tiv e s,  p ro m otin g  A W S c lo ud  t e ch n o lo gie s a n d  c o lla b o ra tio n.V O LU N TEER  E X P ER IE N CEAW S C lo ud  C lu b  C ap ta in&bull;  C o-le d  G DSC ,  o rg an iz in g  e ve n ts  t o  e d uca te  s tu d en ts  o n c lo ud  c o m putin g .G DSC  C o-L ead  a n d  C lo ud  L eadM arc h  2 024 &ndash;  P re se n tA ug ust 2 023 &ndash;  P re se n t',2,2,1,'Recruiter','SL','N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-28 14:32:14',0),(458,111,77,57,NULL,'77_111_177006264066a6569324d1c_Kritika_Aggarwal_resume.pdf','Kritika Aggarwal_resume.pdf','KRITIKA \nAGGARWAL	 	\n 	\nPROFESSIONAL SUMMARY	 	\nA keen, 	well	-organized	, creative and goal	- 	oriented 	intermediate 	possessing excellent 	communication, problem	-solving and 	leadership skills with 	enthusiasm and 	curiosity 	to 	work on new and creative ideas 	in order to explore the world of science.	 	 TECHNICAL COMPETENCIES	 	 	C  	 	C++	 	 	Python 	 	 	Java	 	 	HTML	 	 	CSS	 	 	MySQL	 	 	AutoCAD	 	 	Excel 	 	 	Microsoft Office	  	 	Canva	 	 	Machine Learning	 	 	Data Science	 	 	Data Analysis	 	 	WordPress	 	\nCONTACT	 	\nPHONE:	 	7982994906	 	EMAIL:	 	kritikaagg134@gmail.com	 	LINK	EDIN: 	https://www.linkedin.com/in/kritika	-	aggarwal	-84a944241	 	 \nINTERESTS &amp; HOBBIES	 	\nReading | Dancing | Painting | Playing basketball &amp; volleyball | Listening to musi	c 	 LANGUAGES KNOWN	 	English | Hindi	 	 PERSONAL DETAILS:	 	Mother&rsquo;s name : POOJA AGGARWAL	 	Father&rsquo;s name	 : TARUN AGGARWAL	 	D.O.B. : 1	st March 2004	 	Address : RZ	-18 D/3 West Sagarpur New 	Delhi	 	\n 	\nEDUCATION	 	 	\nIntermediate (CBSE) | LORETO CONVENT SCHOOL DELHI CANTT	 	Session : 2020	-2021	 | Percentage : 9	3% 	 Matriculation (CBSE) | LORETO CONVENT SCHOOL DELHI 	CANTT	 	Session : 2018	-2019 | Percentage : 91.6%	 	 GRADUATION | SHARDA UNIVERSITY GREATER NOIDA	 	Session: 2021	-2025 | CGPA : 8.	924	  	\nCERTIFICATONS	 	\n&bull; 	Course of MACHINE VISION AND ROBOTICS | Sharda Skills	 	(18th April 2022 	&ndash; 28th May 2022)	 	o 	Bar code reading 	machine concepts	 	o 	QR generators and reading concepts	 	 	&bull; 	ARTIFICIAL INTELLIGENCE 	COURSE	 | VERZEO	 	(1st July	 2022 	&ndash; 31th August 	2022)	 	 EXPERIENCES	 	 	&bull; 	5TH TECHNOVATION HACKATHON	 | SHARDA UNIVERSITY	 	(30	th January 2023 	&ndash; 4th February 2023)	 	 	&bull; 	Internship at Metro Telworks 	 	(5th June 2023 	&ndash; 14th July 2023)	 	 	&bull; 	Internship at BanBanjara	 	(11th August 2023 	&ndash; 11th Nove	mber 2023)	 	 PROJECTS	 	 	&bull; 	Dog Breed Classification | VERZEO	 	o 	Machine learning 	&ndash; image recognition	 	 	&bull; 	Abnormal Event Detection in Surveillance videos	 	o 	Machine learning 	&ndash; CNN, image recognition	 	 	&bull; 	Cryptocurrency Prediction 	Using GRU and LSTM	 	o 	Machine learning 	&ndash; GRU, LSTM, pattern recognition	 	 	&bull; 	T-Shirt Designer 	Website	 	o 	Web Development 	&ndash; React, HTML	, CSS	 	\nINTERPERSONAL SKILLS	 	\nTeam Leadership | Management &amp; Coordination | Decision Making | Communication and emotional intelligence | Critical thinking	 | Intellectual 	Curiosity 	| Public Speaking',2,2,1,'Recruiter','SL','N',14,1,'2024-08-01 06:26:34','2024-07-29 06:26:34',0,NULL,NULL,'2024-07-28 14:32:51',0),(459,111,77,58,NULL,'77_111_136266061866a6569325481_MoiezResume.pdf','MoiezResume.pdf','/  	\nS Y ED  M OH AM MAD  M OIE Z  U R R A H M AN\nASP IR IN G D ATA  A N ALY ST	\nM OIE Z R16 1	w ww.lin ke d in .c o m /in /s y e d -m -m oie z-u r-\nr a h m an -9 a1a 17 2 4 8	\nhttp s:/ /g it h ub .c o m /M oie zR	O KH LA , N EW  D ELH I	\nP R O JEC TS\nAuth ore d  R ese arc h  P ap er T it le d  &quot; C hro n ic  K id n ey D is ea se	 	\nP re d ic tio n  u sin g M ach in e L e arn in g: F eatu re  S ele ctio n &quot;\nPap er c u rre n tly  u n d er p ro ce ss	\nS ele cte d  t h e b est 1 0  f e atu re s f ro m  t h e a va ila b le  2 6  f e atu re  s p ace  u sin g	 	\nS ele ctK Best m eth o d .\nR ed uce d  t h e r is k  o f o ve rfit tin g  o n d ata  m od el  b y lo w erin g  t h e d im en sio n a n d	 	\nth e f e atu re  s p ace  o f t h e d ata se t.\nC om pare d  t w o d if fe re n t m od els ,  t r a in in g  b oth  o n t h e b est 1 0  f e atu re s	 	\nse le cte d :  G au ssia n  N aiv e  B aye s a n d  K\nA ch ie ve d  t h e h ig hest a ccu ra cy u sin g  t h e K	 	\nT e ch niq ue (X G Boost),  9 8% .	\nS peech  E m otio n  D ete ctio n  w it h  F u zzy  c la ssi\nPro je ct L in k	\nTra in ed  a  n eura l  n etw ork  m od el  t o  d ete ct t h e e m otio n in  a u d io  f ile s o f	 	\np eo p le &#039;s  s p eech .\nN eura l  N etw ork  w as t r a in ed  o n t h e L o ng  S ho rt T e rm  M em ory  m od el  t o	 	\nc a p tu re  e ffe ctiv e  d ep en d en cie s in  p ast d ata .\nT ES S d ata se t w as u se d  f o r t h e d eve lo p m en t o f t h is  p ro je ct.	\nU ni	 	\nan d F acia l A naly sis  ( c u rre n tly  o n -g oin g)\nIn te g ra tin g  t e xt,  s p eech  a n d  f a cia l  a n aly sis  o n a  s in g ula r b acke n d  p la tfo rm .	\nRob erta  m od el  f ro m  t h e H ug gin g  F ace  lib ra ry  b y M eta  f o r s e n tim en t a n aly sis	 	\no f d ata  in  t e xtu al  f o rm at o r &quot; c h ats &quot;\nS elf  d eve lo p ed  m od el  ( m en tio ned  a b ove ) f o r t h e s e n tim en t a n aly sis  o f d ata  in	 	\na u d io  f o rm at o r &quot; s p eech &quot;\nO pen C V lib ra ry  u se d  f o r t h e s e n tim en t a n aly sis  o f f a cia l  e xp re ssio ns.	\nE D U CATIO N\nSH ARD A U NIV ER SIT Y  |  G REATE R  N OID A, U TTA R	 	\nPR A D ES H\nBac h elo r o f T e ch nolo g y i n  C om pute r S cie n ce	\n10/2021 	&nbsp;	\nGP A	\n8.0 1	10 .0	\nR ele va n t C ours e w ork :  D ata b ase  M an ag em en t S yste m s,  N eura l  N etw ork s,	 	\nO pera tin g  S yste m s,  C om pute r N etw ork s,  In tro d uctio n t o  E ntre p re n eurs h ip ,	 	\nD eep  le arn in g	\nIN TE R N ATIO N AL I N DIA N  S C H OOL A l- J ubail   |  K S A\nHig h S ch ool	\n04/200 7 	&nbsp;	\nCla ss X IIt h :  8 3 %\nC la ss X th :  8 2.6 %	\nS U M MARY\nDeta il  a n d  g oal  o rie n te d  d ata  d riv e n  in d iv id ual  w it h  a	 	\np assio n in  e xp lo rin g  p atte rn s a n d  d ep en d en cie s	 	\nam ong  v a rio us la rg e d ata se ts .  O rg an iz in g ,	 	\npre p ro ce ssin g  a n d  c le an in g  d ata se ts  o f v a ry in g  s iz e	 	\nto  e ffic ie n tly  u se  t h em  a n aly sis  a n d  p re d ic tio ns	 	\nle ve ra g in g  v a rio us t e ch nic a l  t o ols .\nA C H IE V EM EN TS	\nHac kath on  W in ner i n  t h e D ata  S cie n ce	 	\nD om ain\nW on a  h acka th o n c o nd ucte d  in  a  S hard a	 	\nU niv e rs it y  b ootc a m p in  t h e D ata  S cie n ce	 	\nd om ain .\nW in ner o f S park ath on &#039;2 3\nW on a n  in te r- d ep artm en t a p tit u d e a n d  s o ft	 	\ns k ills  b ase d  c o m petit io n.\nT e ch noK ra ts  C od in g  C om petit io n  F in alis t\nR each ed  t h e f in al  r o un d  o f p ro b le m  s o lv in g	 	\nb ase d  c o d in g  c o m petit io n o rg an iz e d  in  S hard a	 	\nU niv e rs it y .\nB est D ele g ate  a w ard  a t I IS M UN &#039;2 0\nJo rd an  b ase d  v ir tu al  M od el  U nit e d  N atio ns.	\nS KIL LS\nData  P re p ro ce ssin g  a n d  A naly sis\nA dva n ce d  E xc e l	P ow erP oin t	P yth on	\nM ac h in e l e arn in g	S Q L	\nSta tis tic al A naly sis	D ata  V is u aliz a tio n	\nJav a	E ffe ctiv e  C om munic atio n	\nTe am  C olla b ora tio n\nA C TIV IT IE S	\nCon te n t W rit in g, B lo g ger\nat 	W is e rY o u th .C lu b	\nR ead in g\nS w im min g\nTra v e lin g	\nwww .enhancv .com	\n	\nPow er ed b y	\nE		q	\nq		\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;\n&bull;	\n\n\n\n\n\n\n\n',2,2,1,'Recruiter','SL','N',14,1,'2024-08-01 06:29:25','2024-07-29 06:29:25',0,NULL,NULL,'2024-07-28 14:32:51',0),(460,112,77,56,NULL,'77_112_33183043366a656b3273ef_Abhinav_.pdf','Abhinav .pdf','Abhinav	 Singh	 	\n2021470897.abhinav	@	.ug.sharda.ac.in	 ❖	 (+91	) 93190	-61178	 ❖	 Gorakhpur	, U.P.	 	\n❖	 LeetCode link	 ❖	 	 	\n \n \n \n \nTECHNICAL SKILLS: 	JAVA, JavaScript, HTML, CSS, 	Tailwind, 	Node.js, Express.js, PostgreSQL, React, Git, 	\nMarkup	 	\n  \n \nMERN Image Generation Website	  	 	 	 	 	                     	 Oct. 20	23	 &ndash; Dec .2023	 	\n▪ 	Independently conceptualized, designed, and developed a sophisticated image generation platform using the \nMERN (MongoDB, Express.js, React.js, Node.js) stack.	 	\n▪ 	Integrated cutting	-edge artificial intelligence capabilities by leveraging the OpenAI API for advanced image 	\ncreation, enhancing the platform&#039;s functionality and appeal.	 	\n▪ 	Employed MongoDB as the primary database solution to efficiently manage and store user data, ensuring \nseamless retrieval and manipulation of generated images.	 	\n  	\n					 	 	                    			 	\n▪ 		 	\n▪ 				 	\n	 	\n▪ 	 \nHTTP requests efficiently.	 	\n \nEDUCATION 	 	 	 	\nSharda 	University	  	 	 	 	 	                     	         	 	 	        	     	Aug .2021 	&ndash; Present 	 	\nB.tech	, Computer Science	  	 	 	 	    	 	 	     	        	     	           	Greater Noida	, U.P.	 	\n▪ 	CGPA 	&ndash; 8.9	 	 	 	 	 	 	 	 	 	         	     	Up until 	6th Semester	 	\n▪ 	Related Coursework : 	Computer Networks 	Operati	ng	 System, Database Management	 System, etc.	 	\n \nRam	-eesh International School (CBSE)	 	 	 	                     	         	              	April	 .20	19	 &ndash;July 2020	   	\nClass 12th	 	 	 	 	    	 	 	     	        	       	         	                        	Greater Noida	, U.P.	 	\n▪ 	Percentage	 &ndash; 84	 % 	\n 	\nRam	-eesh International School (CBSE)	 	 	 	                     	         	              	April .2017 	&ndash;July 2018  	 	\nClass 10th	 	 	 	 	    	 	 	     	        	       	                                 	Greater Noida	, U.P.	 	\n▪ 	Percentage 	&ndash; 91	 % 	\n  	\nCERTIFICATIONS, 	SKILLS &amp; INTERESTS	  	 \n▪ 	Certifications: 	JAVA Foundation	 	\n▪ 	Skills	 : Proficient in DSA, Strong Computer Science Fundamentals,	 Efficient at communicating issues,	 	\n Good	 System Design Understanding	 	\n▪ 	Interests	 : 						  	\n 	\n 	\nI am a Full Stack Developer with a passion for problem	-solving and a curious mindset. I embrace feedback 	\ngracefully, integrating it into my work to continually improve. Eager to expand my skills, I approach each task \nwith a willingness to learn and adapt w	ithin the job. My approachable nature and ability	 communicate ideas make 	\nme a valuable team member, fostering a collaborative environment where innovation thrives.	 	\nFeb . 202	4 &ndash; Mar .202	4	\nCompetitive Chess, Reading Technical Blogs, Linux Ricing , Avid Movie Critique	\nDeveloped a web scraping application using Node.js to generate internal links reports for websites.\nUtilized popular Node.js libraries such as 	`node-fetch	` for making HTTP requests and 	`jsdom	` for	\nImplemented URL normalization to ensure consistent URL formats using the Node.js URL object.	\n			\nGitHub link	\nparsing HTML responses.	\nWeb Cra	wler and Internal Links Analy	zer using Node.js',2,2,1,'Recruiter','SL','N',14,1,'2024-08-01 06:17:16','2024-07-29 06:17:16',0,NULL,NULL,'2024-07-28 14:33:23',0),(461,112,77,NULL,NULL,'77_112_161702027566a656b327bd2_Mohit_2021001021_Resume.pdf','Mohit_2021001021_Resume.pdf','MOHIT SARASWAT\r\nmohitsaraswat1703@gmail.com | (+91) 8130697855\r\nGreater Noida,India\r\nMohitSaraswat17 MohitSaraswat/ MohitSaraswat\r\nPROFESSIONAL SUMMARY\r\nPassionate and results-driven 4th-year Computer Science Engineering student specializing in full-stack\r\ndevelopment. Skilled in data structures and algorithms with a proven ability to solve complex problems and lead\r\nprojects to successful completion. Eager to leverage technical skills in innovative technology solutions.\r\nSKILLS\r\nTechnical Skills:- Full Stack Web Development (MongoDB,NodeJs,ReactJs,ExpressJs), MySql, Java, HTML, CSS,\r\nJavaScript\r\nCourse Work :- Data Structures and Algorithms, Computer Networks,DBMS,OOPs,Operating System.\r\nTools:- Git , Github, VS Code, MS Office, Oracle Cloud Infrastructure , Google WorkSpace\r\nSoft Skills:- Decision Making, Creative Thinking, Research&Analysis, Project Management\r\nEDUCATION\r\nBtech Computer Science | Sharda University ,Greater Noida CGPA: 8.358| (2021-25)\r\nXII (CBSE) | Father Agnel School, Greater Noida Percentage: 84%| 2021\r\nACADEMIC PROJECTS\r\nTask Management WebApp(TaskSync)\r\nDeveloped a MERN stack task management app with secure authentication, CRUD operations, and Cloudinary\r\nintegration for image uploads. Showcased expertise in React, MongoDB, ExpressJS, and NodeJS.\r\nTextUtils\r\nCreated TextUtils, a React web app for text manipulation, including case conversion, space removal, and word\r\ncount. Features include text preview, copy functionality, and light/dark modes for readability.\r\nStress And Anxiety Detection\r\nDeveloped a real-time stress and anxiety detection system using Python, OpenCV, and deep learning for emotion\r\nrecognition. Integrated live video monitoring with stress indicators, showcasing skills in image processing, facial\r\nrecognition, and machine learning.\r\nReact Portfolio Website\r\nCreated a personal portfolio website using ReactJS and Tailwind CSS, featuring a modern, responsive design and\r\ninteractive elements. Demonstrated expertise in frontend development and design frameworks.\r\nACHIEVEMENTS / CERTIFICATIONS\r\nResearch Paper:- Presented my paper on stress and anxiety detection at ICTACS 2023, showcasing a real-time\r\nfacial expression analysis system using Python and deep learning. The paper is published by IEEE.\r\nUdemy:- Java In-Depth : Become Complete Java Engineer!\r\nOracle Academy:- Database Foundations | Database Programming with SQL\r\nDevTown:- JavaScript and React\r\nINTERESTS / HOBBIES\r\nGaming\r\nTravelling\r\nCooking',0,0,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-28 14:33:23',0),(462,113,77,54,NULL,'77_113_188343306466a656d533536_AbhiAkshat-CV.pdf','AbhiAkshat-CV.pdf','Abhi Akshat	 	(Aspiring	 	Engineer)	 	\nLinkedIn:	 	linkedin.com/in/abhi	-	akshat	-	56473a228	 	\n 	\n       	abhiakshatofficial@gmail.com	 	\n+91	-	7001356029	\nGitHub:	 	github.com/abhi	-	akshat	 	\n \nSKILLS	 	SUMMARY	 	\nLanguages	 	C/C++,	 	JavaScript,	 	Python, JAVA, GoLang, HTML, CSS, Swift	, SQL	 	\nFrameworks	 	P	yT	orch, TensorFlow	, OpenCV, 	NodeJS, React	J	S	, Echo, SwiftU	I	, StreamLit	 	\nTools	 	VS Code, GIT, GitHub, Google Colab, AWS, Docker	, PostgreSQL, MongoDB	 	\nPlatform	 	Web, Cloud, iOS	 	\nSoft Skills	 	Project Management, Leadership, Teamwork, Writing, Public Speaking	 	\n \nEXPERIENCE	 	\nSoftware Developer 	Intern @ 	SKILLMONDE	 	Greater Noida	 	\nInternship	 	 	June	 	202	4	 	&ndash;	 	Present	 	\n&bull;	 	Developed Internal Automation to st	ream	li	ne w	ork	flow using AI. Reducing time taken 	by 	6	0	-	70%.	 	-	 	Pyth	on	 	\n&bull;	 	Redesigning of 	websi	te and 	developing n	ew features such as 	&ndash;	 	Subscription 	System	.	 	&ndash;	 	ReactJ	S, 	NodeJS.	 	\n \nAI 	Intern @ COINCENT.AI	 	Remote	 	\nInternship	 	 	July 202	3	 	&ndash;	 	August 202	3	 	\n&bull;	 	Developed a landmark detection application using Google Landmarks V2 dataset. 	Built	 	and Trained a	 	VGG	-	\n19 model.	 	\n&bull;	 	Used technologies like TensorFlow and Streamlit. Python was the primary language for the project.	 	\n \nPROJECTS	 	\n&bull;	 	Dynamic Object 	Detector	 	 	MERN with Typescript	 	\no	 	Developed a 	memory efficient system that detects moving object. Built using C++ and OpenCV. Added 	\nefficient memory management. 	Winner	 	of BGSW AutoVision X hackathon. 	 	\no	 	Main focus of the project was memory efficiency.	 	\n&bull;	 	ML Preprocessor CLI	 	CLI Tool, AI	 	\no	 	Developed 	a	 	No	-	Code	 	preprocessor  that  handles  preprocessing  needs  of 	any  ML  projects.	 	Developed 	\nfeature  like  dataset  description,  handling  null  values, 	Encoding  Categorical  Data	, 	Feature  Scaling	 	and 	\nDownloading modified dataset.	 	\no	 	Deployed it on an EC2 instance in AWS.	 	Built using Python and libraries like 	&ndash;	 	Pandas, NumPy, SciKit 	\nLearn.	 	\n \nACHIEVEMENT	 	\n&bull;	 	Winner of BGSW AutoVisionX	 	2024 	&ndash;	 	Hackathon conducted by Bosch Global Software Technologies.	 	\n&bull;	 	Published  a  paper,  &ldquo;A  Comparative  Analysis  on  Various  Machine  Learning  Algorithms  Used  for \nHandwritten Digit Recognitions&rdquo;, at ICCIS 2023.	 	\n \nEDUCATION	 	\nSharda University, Greater Noida	 	   	Uttar Pradesh, India	 	\nBachelor Of Technology 	&ndash;	 	Computer Science and Engineering	 	 	July 2021 	&ndash;	 	May 2025	 	\nCGPA 	&ndash;	 	9.1	87	 	\nSpecialization	: Artificial Intelligence and Machine Learning	 	\nNew Light Academy, Namchi	 	Sikkim, India	 	\nCBSE: 82.4%	 	  	March 2020	 	\nDon 	Bosco School, Siliguri	 	West Bengal, India	 	\nICSE: 90.4%	 	 	March 2018',2,2,1,'Recruiter','SL','N',14,1,'2024-08-01 05:31:30','2024-07-29 05:31:30',0,NULL,NULL,'2024-07-28 14:33:57',0),(463,113,77,52,NULL,'77_113_42175591766a656d533b23_Ishan_Sen_Resume.pdf','Ishan Sen_Resume.pdf','Ishan Sen\n\nJollyBolt \ntheishansen \nishansen.in \nsenishan21@gmail.com \n+91 9953774233\nWORK EXPERIENCE\nEYTHOR PVT LTD | Software Developer Intern April 2024 &ndash; July 2024\n&bull;\nSpearheaded the migration of the company&rsquo;s monitoring software from React.jstoNext.js andTypeScript ,\nresulting in a 27%increase in performance.\n&bull;\nEnhanced accessibility by16% andSEO by37% by implementing best practices and optimizing the\napplication.\n&bull;\nDeveloped interactive tables using TanStack Table, improving data visualization and user interaction.\n&bull;\nEmployed ShadCN/UI to improve the design and integrate dark mode.\nSK INFO TECHIES | Software Developer Intern June 2023 &ndash; July 2023\n&bull;\nWorked on a new company portfolio using NextJs.\n&bull;\nImproved SEOperformance by 13%through breadcrumb implementation.\n&bull;\nIntegrated a sophisticated contact form with email services.\nSKILLS\nLanguages: C++, Python, Java, C\nDevelopment: HTML, CSS, JavaScript, Typescript, Tailwind CSS, React JS, Next JS, MongoDB, Express JS, Node JS,\nREST API, Git, Github\nLibraries: Redux Toolkit, Recharts, React Hook Forms, TanStack Table, Framer Motion, BootStrap\nPROJECTS\nINVOICERY Live | Github React Js, Tailwind CSS, MongoDB, Express JS, Node JS\n&bull;\nInvoicery is a GST billing software that enables users to create complex GST billsin under30 seconds .\n&bull;\nEngineered an advanced analytics dashboardproviding detailed insights into key business metrics.\n&bull;\nIntegrated Rechartsto create interactive charts and visualizations.\n&bull;\nImplemented TanStack Table for managing and displaying tabular data effectively.\n&bull;\nUtilized Redux Toolkit for efficient state management and React-HookForm for form handling.\nINOTES Live | Github React Js, Tailwind CSS, MongoDB, Express JS, Node JS\n&bull;\nBuilt a full-stack note-taking website that allows users to create, edit, organize, and delete notes.\n&bull;\nIntegrated robust user authentication mechanisms, employing JWTfor secure access.\n&bull;\nEngineered custom RESTful APIfor efficient data interactions to ensure a seamless and responsive user\nexperience.\nWATCH IT ALL Live | Github React Js, Tailwind CSS, Material UI\n&bull;\nCreated a responsive multimedia platform enabling users to search and view YouTube videos.\n&bull;\nSeamlessly integrated the &rsquo;YouTube v3&rsquo; API.\nEDUCATION\nSharda University, India September 2021 - Expected 2025\nBachelor of Technology in Computer Science Engineering CGPA:8.96\nDelhi Public School,Mathura Road April 2019 - March 2020\nAISSCE (CBSE), CLASS XII Percentage:94.2\nACHIEVEMENTS\n&bull;\nSecured firstplace in Technokrats&rsquo;3.0\n and Technokrats&rsquo;23  .\n&bull;\nSolved over 300DSA problems on LeetCode.\n\n&bull;\nSteered a team of 4 to achieve a top-10ranking in the &rdquo;Smart Health, Agriculture, and Hygiene&rdquo; category at\nthe 5th Technovation Hackathon.\n\n&bull;\nReached Pupilon codeforces.\n\n&bull;\nAwarded Merit Scholarship for ranking undertop-20for 3 consecutive years.',2,2,1,'Recruiter','SL','N',14,1,'2024-08-01 05:29:44','2024-07-29 05:29:44',0,NULL,NULL,'2024-07-28 14:33:57',0),(464,113,77,NULL,NULL,'77_113_14544491366a656d533cd2_Janvi.pdf','Janvi.pdf','Janvi Sharma	 	\nDetail	-oriented and enthusiastic 	software	 	\ndeveloper	 with	 a solid	 foundation	 in com puter	 	\nscience principles and programming languages	 	\nlike	 Python	 and	 Java	 	\nsharmaajanvi9@gmail.com	 	 	\n 	\n9971058561	 	\n 	\nDelhi, 	Noida,	 India	 	\n \n \nEDUCATION	 	\nClass	 X (	CBSE	) 	\nMANI BEN PATEL BHARTIYA MAHILA \nSARVODAYA KANYA VIDHYALAY	 	\n03/2018	 - 05/2019	, 	79.2%	 	\n \nClass	 XII	 (CBSE)	 	\nMANI BEN PATEL BHARTIYA MAHILA \nSARVODAYA KANYA VIDHYALAY	 	\n03/2020	 - 05/2021	, 	81.6%	 	\n \nBachelor	 of	 Technology	 (CSE)	 	\nSharda	 University	 	\n10/2021	 - Present	, 	Second Y ear	 - 7.663 CGPA	 	  \n \nPERSONAL	 PROJECTS	 	\nQuiz	 Game	 - Java	 and	 Spring	 Boot	 	\n(05/2023	 - 05/2023)	 	\nCreated	 a quiz	 game	 using	 Java	 and	 Spring	 boot	 	 \nTO	-DO Web 	Application 	- Java and Spring Boot	 	\n(04/2023	 - 04/2023)	 	\nCreated	 a TO	-DO	 web	 application	 using	 Java	 and	 Spring	 Boot	 	 \nMedistat	 - Java	 and	 Spring	 Boot	 (12/2022	 - 02/2023)	 	\nThis	 application	 stores	 the	 user&#039;s	 medical	 reports	 in an 	organized	 manner	 so that	 they	 could	 be accessible	 eﬃciently	 by 	any	 doctor	 and	 the	 user	 variety	 of features	 to analyze	 the	 data.	 	 \nStudy	 Buddy	 Web	 Application	 - Python	 and	 Django	 	\n(09/2021	 - 11/2021)	 	\nThis application helps the students utilize their time and	 	provide	 them	 with	 all the	 curriculum	 like	 previous	 year	 papers,	 	books,	 notes,	 etc.	 	  \n \nWORK	 EXPERIENCE	 	\nBackend	 Developer(Java)	 - Internship	 	\nFlickMatch	 	\n07/2023	 - Present	, 	Noida,	 India	 	\nSpringBoot	 Project	 	\nDeveloped RESTful APIs for creating events, added	 	\ncurrency	 and	 worked	 on various	 diﬀerent	 scenarios.	 	\nWrote	 unit test cases in	 JUnit5 and Hamcrest.	 	\nSKILLS	 	 	\n 	 	 	 	 	\n 	 	 	 	 	\n 	 	 	 	\n 	\n \n \nACHIEVEMENTS	 	\nTechnovation Hackathon 	- Sharda Univers	ity	 	\n(01/2023	 - 02/2023)	 	\nGot	 certiﬁcate	 of participation	 on smart	 health	 project	 	 \nOlympiad	 of Mathematics	 (05/2011	 - 05/2011)	 	\nFourth	 Rank	 in Class	 	 \nInternational Conference on Recent Trends in	 	\nBiotechnology	 and	 Applied	 Bioinformatics	 (ICRTBAB)	 	\n2023	 - Sharda	 University	 (04/2023	 - 04/2023)	 	\nCertiﬁcate	 of Participation	 	   \n   Successful food drives in NGO	 	\n \nCERTIFICATES	 	\nJava	 Course	 Completion	 by	 Oracle	 Academy	 	\n(12/2022	 - 02/2023)	 	Completion	 of Java	 Course	 	 \nDatabase Completion	 by	 Oracle	 Academy	 	\n(05/2023	 - 07/2023)	 	Completion	 of Database	 Course	 	 \nJava	 Foundation	 By	 Oracle	 Academy	 	\n(07/2022	 - 11/2022)	 	Completion	 of Java	 Foundation	 Course	 	 \n \n \n \n \n \nLANGUAGES	 	\nImplemented	 GraphQL	 queries	 using	 DynamoDB.	 	      	English	 	\nFull	 Professional	 Proﬁciency	 	\nHindi	 	\nFull	 Professional	 Proﬁciency	 	\n \n 	\nINTERESTS	 	\nAdaptability	 	\nProblem	 Solving	 	Time	 Management	 	Leadership	 	\n \n 	\nTeam work	 	\n 	\nMySQL	 	Educator	 	Data Entry	 	\nData	 Structures	 	C 	Java	 	Python	 	\nCricket	 	Coding',2,2,1,'Recruiter','SL','N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-28 14:33:57',0),(465,113,77,53,NULL,'77_113_81585760766a656d533ec8_jyoti_harbola_resume.pdf','jyoti harbola resume.pdf','JYOTI	 HARBOLA	 	\nJyotiharbola2016@gmail.com	 | +91	 8269639607	 	\n 	\n  Leetcode 	-Jyotiharbola2016	 	  /Jyoti	-Harbola	 	\nSUMMARY	 	\nPassionate  and  dedicated  Software  Developer	 with  expertise  in  Java,  Data  Structures,  and 	\nAlgorithms.  Proven  proficiency  in  web  development  using  React  and  Node.js.  Skilled  in  building \nresponsive,  dynamic  web  applications	.  Highly  motivated  to  learn  and  grow,  thriving  in  challenging 	\nenvironments, and adept at applying technical skills to solve complex problems.	 	\nEXPERIENCE	 	\n❖  Web	 Development	 | Pyramid	 Cyber	 	June 	2024	 	\nCollaborated with a team of developers to design and implement user	-friendly interfaces.	 	\nDeveloped	 and	 maintained	 responsive	 web	 applications	 using	 React	 and	 Node.js,	 ensuring	 	\nhigh	 performance	 and	 scalability.	 	\n❖ Frontend	 Development	 	April	 2022	 	\nCreated visually appealing and user	-friendly interfaces using HTML, CSS, and JavaScript.	 Ensured	 	\nseamless	 cross	-browser	 compatibility	 and	 mobile	 responsiveness	 for all	 web applications.	 	\n❖ Research	 Paper	 | Stress	 and	 Anxiety	 Detection	 via	 facial	 expression	 	Dec 2023	 	\nPublished 	a research paper in the 2023 3rd International Conference	 on	 Technological	 	\nAdvancements	 in Computational Sciences	 (ICTACS).	 	\n \nACADEMIC	 PROJECTS	 	\n 	\n❖ Path	 Finding	 Algorithm	 Visualizer	 	\nDeveloped a tool to visualize and demonstrate various pathfinding algorithms, enhancing user	 	\nunderstanding	 of algorithmic processes	 and	 efficiencies.	 	\n 	\n❖ IOT	 Based	 water	 Quality	 Monitoring	 System	 	\nImplemented a system using sensors to measure and monitor water quality parameters,	 	\nassessing	 water	 safety and	 usability in real	-time.	 	\n 	\n❖ Dog	 Care	 web	 Application	 for	 NGO	 	\nDesigned	 and	 built	 a web application	 for	 &ldquo;TheProjectDaisy,&rdquo;	 a local	 college NGO,	 to manage	 	\nand	 support dog	 care	 initiatives.	 	\n 	\n❖ Voice	 Based	 Emailing	 System	 for	 Blinds	 	\nCreated	 a voice	-based	 system	 to transcribe	 user	 voice	 commands	 into	 text,	 facilitating	 email	 	\ncomposition	 and	 management for	 individuals	 with	 visual impairments.	 	\n \nEDUCATION	 	\n❖	 Computer	 Science |	 Sharda	 University	 	CGPA	: 8.2	 	\n❖	 XII	 (CBSE)	 | Army	 Public	 School	 	76.8%	| 2020	 	\nEXTRACURRICULAR	 ACTIVITIES	 	\n&bull; 	Hackathon	 Hospitality	 Team	 Member.	 	\n&bull; 	Active	 Member of	 the	 NGO	 &ldquo;The	 Project	 Daisy&rdquo;',2,2,1,'Recruiter','SL','N',14,1,'2024-08-01 05:30:57','2024-07-29 05:30:57',0,NULL,NULL,'2024-07-28 14:33:57',0),(466,113,77,55,NULL,'77_113_120449631666a656d53409f_Kritika_Tripathi_Resume.pdf','Kritika_Tripathi_Resume.pdf','Kritika Tripathi\nEmail: kritika.tripathi03@gmail.com\nLinkedIn: linkedin.com/in/kritika-tripathi-b5052a186/ Mobile: +91-7011435021\nGithub: github.com/kritikatripathi03\nSkills Summary &bull;\nLanguages : Python, C++, C, JavaScript, HTML, CSS, MySQL, JAVA\n&bull; Frameworks : React, Angular, Bootstrap, Flask, Django\n&bull; Tools : VS Code, PyCharm, Anaconda; Docker, GIT, Bash, Google Colab, Google Cloud\n&bull; Platforms : Web, Windows, Cloud, Linux\n&bull; Soft Skills : Leadership, Teamwork, Event Management, Writing, Public Speaking, Time Management\nEducation &bull;\nSharda University\nUttar Pradesh, India\nBachelor of Technology - Computer Science; CGPA: 9.285 October 2021 - June 2025\nSpecialisation: Artificial Intel ligence, Machine Learning\n&bull; Delhi Public School, Maruti Kunj\nHaryana, India\nClass 12th 92.2 %; Class 10th 95.8 % March 2018 - March 2021\nExperience &bull;\nSDE Intern @Amazon-Team CMT Systems\nBengaluru, Karnataka\nInternship May&rsquo;24 - July&rsquo;24\n◦ Worked on a Marketplace Onboarding into the CMT Systems by adding config changes.\n◦ Worked on the core problem faced by CMT Systems where the price is not assigned to products.\n◦ Learned to work on, test, and perform production-level deployment on pipelines. Learned to create version sets\nand do config changes.\n&bull; Data Analyst Intern @NoQs Digital\nRemote\nInternship Mar&rsquo;23 - July&rsquo;23\n◦ Used data to create models that depict trends in the customer base and the consumer population as a whole.\n◦ Developed and maintained databases, and data systems &ndash; reorganized data in a readable format.\n&bull; Google Developer Student Clubs(GDSC) Tech Lead\nRemote\nCore Team Member Jan&rsquo;22 - Jun&rsquo;22\n◦ Conducted online and offline technical &amp; soft-skills training, and coding contest for students, especially freshers.\n◦ Helped freshers get familiar with the club and solved their queries.\nProjects &bull;\nDynamic Ob ject Detection (C++ - CMake, OpenCV) :\nCreate a tool that detects dynamic ob jects on the road. Employed capabilities of OpenCV module in C++ to detect ob jects.\nLearned and attempted to enhance memory management in the pro ject using capabilities of C++ language. (February&rsquo;24)\n&bull; Responsive Portfolio Website(React, CSS, Bootstrap) :\nWorked on a website that showcases personal portfolio. It contains the About, Skills, and Pro jects sections, and a contact\nform. Worked with Bootstrap to make the website responsive. (Mar &rsquo;23)\n&bull; Handwritten Digit Recognition Using 5 Different Algorithms (Python - Machine Learning) :\nLearned how to recognize handwritten digits using 5 different algorithms. Used MNIST and SKLearn load digits datasets. CNN\nachieved the highest accuracy of 99.56 percent and 97.15 percent on training data and testing data respectively. (April &rsquo;23)\n&bull; YouTube Transcript Summariser (Python, HTML, CSS, Javascript) :\nAn extension that would take up the transcript of any video available on YouTube whose transcript is present and summarise\nits transcript for the user. I used. I learned to use the Flask framework and used T5tokeniser for summarising the transcript.\n(December &rsquo;23)\nAchievements &bull;\nBosch Global Software Technologies AutoVisionX Hackathon 2024 Winner :\nBagged the First Prize in the AutovisionX Hackathon held from December 2024 to March 2024. Create a Dynamic Ob ject\nDetection tool with efficient memory management unit.\n&bull; IEEE 3rd International Conference on TESMET 2023 :\nPresented the paper titled - &rdquo;Enhancing the Security of Online Voting System Using Defined Biometrics&rdquo;, which discusses\nimproving the voting system and shifting to online voting. The paper has been accepted and published in IEEE Xplore.\n&bull; International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) 2023 :\nPresented the paper titled - &rdquo;A Comparative Analysis of Various Machine Learning Algorithms for Handwritten Digit\nRecognition&rdquo;, which compares 5 popular algorithms for handwritten digit recognition. The paper has been accepted and\npublished in IEEE Xplore.\n&bull; Women Engineers Program Scholar :\nSelected for a two-year experiential program among 250 women engineering students from nearly 30,000 applicants all-over\nIndia. TalentSprint conducts it in collaboration with Google.',2,2,1,'Recruiter','SL','N',14,1,'2024-08-01 05:32:02','2024-07-29 05:32:02',0,NULL,NULL,'2024-07-28 14:33:57',0),(467,114,77,NULL,NULL,'77_114_80960437566a707c281f90_Abhinav_.pdf','Abhinav .pdf','Abhinav	 Singh	 	\n2021470897.abhinav	@	.ug.sharda.ac.in	 ❖	 (+91	) 93190	-61178	 ❖	 Gorakhpur	, U.P.	 	\n❖	 LeetCode link	 ❖	 	 	\n \n \n \n \nTECHNICAL SKILLS: 	JAVA, JavaScript, HTML, CSS, 	Tailwind, 	Node.js, Express.js, PostgreSQL, React, Git, 	\nMarkup	 	\n  \n \nMERN Image Generation Website	  	 	 	 	 	                     	 Oct. 20	23	 &ndash; Dec .2023	 	\n▪ 	Independently conceptualized, designed, and developed a sophisticated image generation platform using the \nMERN (MongoDB, Express.js, React.js, Node.js) stack.	 	\n▪ 	Integrated cutting	-edge artificial intelligence capabilities by leveraging the OpenAI API for advanced image 	\ncreation, enhancing the platform&#039;s functionality and appeal.	 	\n▪ 	Employed MongoDB as the primary database solution to efficiently manage and store user data, ensuring \nseamless retrieval and manipulation of generated images.	 	\n  	\n					 	 	                    			 	\n▪ 		 	\n▪ 				 	\n	 	\n▪ 	 \nHTTP requests efficiently.	 	\n \nEDUCATION 	 	 	 	\nSharda 	University	  	 	 	 	 	                     	         	 	 	        	     	Aug .2021 	&ndash; Present 	 	\nB.tech	, Computer Science	  	 	 	 	    	 	 	     	        	     	           	Greater Noida	, U.P.	 	\n▪ 	CGPA 	&ndash; 8.9	 	 	 	 	 	 	 	 	 	         	     	Up until 	6th Semester	 	\n▪ 	Related Coursework : 	Computer Networks 	Operati	ng	 System, Database Management	 System, etc.	 	\n \nRam	-eesh International School (CBSE)	 	 	 	                     	         	              	April	 .20	19	 &ndash;July 2020	   	\nClass 12th	 	 	 	 	    	 	 	     	        	       	         	                        	Greater Noida	, U.P.	 	\n▪ 	Percentage	 &ndash; 84	 % 	\n 	\nRam	-eesh International School (CBSE)	 	 	 	                     	         	              	April .2017 	&ndash;July 2018  	 	\nClass 10th	 	 	 	 	    	 	 	     	        	       	                                 	Greater Noida	, U.P.	 	\n▪ 	Percentage 	&ndash; 91	 % 	\n  	\nCERTIFICATIONS, 	SKILLS &amp; INTERESTS	  	 \n▪ 	Certifications: 	JAVA Foundation	 	\n▪ 	Skills	 : Proficient in DSA, Strong Computer Science Fundamentals,	 Efficient at communicating issues,	 	\n Good	 System Design Understanding	 	\n▪ 	Interests	 : 						  	\n 	\n 	\nI am a Full Stack Developer with a passion for problem	-solving and a curious mindset. I embrace feedback 	\ngracefully, integrating it into my work to continually improve. Eager to expand my skills, I approach each task \nwith a willingness to learn and adapt w	ithin the job. My approachable nature and ability	 communicate ideas make 	\nme a valuable team member, fostering a collaborative environment where innovation thrives.	 	\nFeb . 202	4 &ndash; Mar .202	4	\nCompetitive Chess, Reading Technical Blogs, Linux Ricing , Avid Movie Critique	\nDeveloped a web scraping application using Node.js to generate internal links reports for websites.\nUtilized popular Node.js libraries such as 	`node-fetch	` for making HTTP requests and 	`jsdom	` for	\nImplemented URL normalization to ensure consistent URL formats using the Node.js URL object.	\n			\nGitHub link	\nparsing HTML responses.	\nWeb Cra	wler and Internal Links Analy	zer using Node.js',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-29 03:08:50',0),(468,114,77,NULL,NULL,'77_114_132377760266a707c28252b_Mohit_2021001021_Resume.pdf','Mohit_2021001021_Resume.pdf','MOHIT SARASWAT\nmohitsaraswat1703@gmail.com | (+91) 8130697855\nGreater Noida,India\nMohitSaraswat17 MohitSaraswat/ MohitSaraswat\nPROFESSIONAL SUMMARY\nPassionate and results-driven 4th-year Computer Science Engineering student specializing in full-stack\ndevelopment. Skilled in data structures and algorithms with a proven ability to solve complex problems and lead\nprojects to successful completion. Eager to leverage technical skills in innovative technology solutions.\nSKILLS\nTechnical Skills:- Full Stack Web Development (MongoDB,NodeJs,ReactJs,ExpressJs), MySql, Java, HTML, CSS,\nJavaScript\nCourse Work :- Data Structures and Algorithms, Computer Networks,DBMS,OOPs,Operating System.\nTools:- Git , Github, VS Code, MS Office, Oracle Cloud Infrastructure , Google WorkSpace\nSoft Skills:- Decision Making, Creative Thinking, Research&Analysis, Project Management\nEDUCATION\nBtech Computer Science | Sharda University ,Greater Noida CGPA: 8.358| (2021-25)\nXII (CBSE) | Father Agnel School, Greater Noida Percentage: 84%| 2021\nACADEMIC PROJECTS\nTask Management WebApp(TaskSync)\nDeveloped a MERN stack task management app with secure authentication, CRUD operations, and Cloudinary\nintegration for image uploads. Showcased expertise in React, MongoDB, ExpressJS, and NodeJS.\nTextUtils\nCreated TextUtils, a React web app for text manipulation, including case conversion, space removal, and word\ncount. Features include text preview, copy functionality, and light/dark modes for readability.\nStress And Anxiety Detection\nDeveloped a real-time stress and anxiety detection system using Python, OpenCV, and deep learning for emotion\nrecognition. Integrated live video monitoring with stress indicators, showcasing skills in image processing, facial\nrecognition, and machine learning.\nReact Portfolio Website\nCreated a personal portfolio website using ReactJS and Tailwind CSS, featuring a modern, responsive design and\ninteractive elements. Demonstrated expertise in frontend development and design frameworks.\nACHIEVEMENTS / CERTIFICATIONS\nResearch Paper:- Presented my paper on stress and anxiety detection at ICTACS 2023, showcasing a real-time\nfacial expression analysis system using Python and deep learning. The paper is published by IEEE.\nUdemy:- Java In-Depth : Become Complete Java Engineer!\nOracle Academy:- Database Foundations | Database Programming with SQL\nDevTown:- JavaScript and React\nINTERESTS / HOBBIES\nGaming\nTravelling\nCooking',0,1,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-07-29 03:08:50',0),(469,115,77,59,NULL,'77_115_197236027766a73687eb2ca_MOHIT_SARASWAT.docx','MOHIT SARASWAT.docx','\r\nMOHIT SARASWAT\r\nmohitsaraswat1703@gmail.com | (+91) 8130697855\r\nGreater Noida,India\r\nMohitSaraswat17 MohitSaraswat/MohitSaraswat\r\nPROFESSIONAL SUMMARY\r\nPassionate and results-driven 4th-year Computer Science Engineering student specializing in full-stack development. Skilled in data structures and algorithms with a proven ability to solve complex problems and lead projects to successful completion. Eager to leverage technical skills in innovative technology solutions.\r\nSKILLS\r\nTechnical Skills:- Full Stack Web Development (MongoDB,NodeJs,ReactJs,ExpressJs), MySql, Java, HTML, CSS, JavaScript\r\nCourse Work :- Data Structures and Algorithms, Computer Networks,DBMS,OOPs,Operating System.\r\nTools:- Git , Github, VS Code, MS Office, Oracle Cloud Infrastructure , Google WorkSpace\r\nSoft Skills:- Decision Making, Creative Thinking, Research&amp;amp;Analysis, Project Management\r\nEDUCATION\r\nBtech Computer Science | Sharda University ,Greater Noida   CGPA:  8.358 (2021-25)\r\nXII (CBSE) | Father Agnel School, Greater NoidaPercentage:  84% 2021\r\nACADEMIC PROJECTS\r\nTask Management WebApp(TaskSync)\r\nDeveloped a MERN stack task management app with secure authentication, CRUD operations, and Cloudinary integration for image uploads. Showcased expertise in React, MongoDB, ExpressJS, and NodeJS.\r\nTextUtils\r\nCreated TextUtils, a React web app for text manipulation, including case conversion, space removal, and word count. Features include text preview, copy functionality, and light/dark modes for readability.\r\nStress And Anxiety Detection\r\nDeveloped a real-time stress and anxiety detection system using Python, OpenCV, and deep learning for emotion recognition. Integrated live video monitoring with stress indicators, showcasing skills in image processing, facial recognition, and machine learning.\r\nReact Portfolio Website\r\nCreated a personal portfolio website using ReactJS and Tailwind CSS, featuring a modern, responsive design and interactive elements. Demonstrated expertise in frontend development and design frameworks.\r\nACHIEVEMENTS / CERTIFICATIONS\r\nResearch Paper:- Presented my paper on stress and anxiety detection at ICTACS 2023, showcasing a real-time facial expression analysis system using Python and deep learning. The paper is published by IEEE.\r\nUdemy:-Java In-Depth : Become Complete Java Engineer!\r\nOracle Academy:-Database Foundations | Database Programming with SQL\r\nDevTown:-JavaScript and React\r\nINTERESTS / HOBBIES                                                                  \r\nCooking\r\nTravelling                                                                                                       \r\n',2,2,1,'Recruiter','SL','N',14,1,'2024-08-01 06:31:47','2024-07-29 06:31:47',0,NULL,NULL,'2024-07-29 06:28:23',0),(470,116,73,41,NULL,'73_116_13905622166ac3b7a82fc9_Vishwas_HMI_Developer.pdf','Vishwas HMI Developer.pdf','Pag e 1  o f 2  \nWORK  E X PER IE N CE :  \nMah in d ra  &amp;  M ah in d ra  L td  (  M ah in d ra  S o ft w are  D efin ed\nVeh ic le  C en te r )  \nSen io r S o ft w are  E n g in eer (  L 7-O pera tio nal  )  \n10 /2 0 23 -  P re se n t\n, \n  Coim bato re  \nFu ll  T im e \nWork e d  a s H M I  D eve lo p er  fo r  M ah in d ra  V eh ic le s a t M SD VC b y E le vatin g  c lu ste r\nfe atu re s w it h  k o tlin  &amp;  J a v a. \nEn han ce d  IV I  M ed ia   ap plic a tio n  b y  im ple m en tin g   ad van ce d  so urc e   se le ctio n\nfe atu re s a n d  f u lf illin g  n ew  r e q uir e m en ts ,  o p tim iz in g  f u nctio nalit y  t o  m eet s p ecif ic\npro je ct o b je ctiv e s. \nSp earh ead ed  th e d eve lo p m en t  o f  S en try  M od e,  P ark  A pp,  M yV eh ic le ,  A m bie n t\nLig hts   IV I \nApplic a tio ns,  in co rp ora tin g   esse n tia l  fe atu re s  to   en han ce   use r\nexp erie n ce  a n d  S ecu rit y . \nSu cce ssfu lly   in te g ra te d   Vid eo   Pla y e r,  Exo   Pla y e r  &amp;  sc re en sh o t  fu nctio n\nim ple m en ta tio ns,  e n ric h in g  th e a p plic a tio n w it h  se am le ss  m ult im ed ia  p la y b ack\nca p ab ilit ie s. \nIm ple m en te d  s e ekb ar f u nctio nalit y  w it h in  t h e a p plic a tio n,  e n han cin g  u se r c o ntro l\nan d  n av ig atio n t h ro ug h v id eo  p la y b ack . \nLe ve ra g ed   JIR A  to   cre ate   co m pre h en siv e   te st  se ts ,  stre am lin in g   th e  te stin g\npro ce ss a n d  e n su rin g  t h o ro ug h c o ve ra g e o f U I e le m en ts  a n d  f u nctio nalit ie s. \nCond ucte d  U I  t e stin g  a n d  in stru m en ta tio n t e stin g  t o  id en tif y  a n d  re ctif y  is su es,\nth ere b y e n han cin g  t h e o ve ra ll  u sa b ilit y  a n d  p erf o rm an ce  o f t h e a p plic a tio n. \nExp ert  in  a ctiv e ly  re so lv in g  B ug s fo r  a  w id e a rra y  o f  a p plic a tio ns,  s p ecia liz in g  in\nboth   se lf - d eve lo p ed   ap plic a tio ns  an d   fo r  In -V eh ic le   In fo ta in m en t  (  IV I  )\nap plic a tio ns. \nUtiliz e d  C A N oe a n d  V eh ic le  S p y s o ft w are  to  p erf o rm  c o m pre h en siv e  h ard w are\nte stin g  o f (C A N )  a n d  (E C U )  s ig nals  f o r V eh ic le  T e stin g ,  e n su rin g  s y ste m  r e lia b ilit y\nan d  f u nctio nalit y  t h ro ug h d eta ile d  s ig nal  a n aly sis  a n d  t ro ub le sh o otin g . \nCap gem in i  En g in eerin g   (  R &amp; D Su b sid ia ry   of \nCap gem in i\nGro u p  )  \nSo ft w are  E n g in eerin g  L e ad  (  P ro fe ssio nal  I / B 1 )  \n04/2 0 23 -  1 0 /2 0 23\n, \n  Ban g alo re  \nFu ll  T im e \nSp ecia liz e d   in   co nnectiv it y   wit h   Apple   devic e s  an d   work e d   on  Apple &#039;s\nte ch no lo gie s s u ch  a s iA P1,  iA P2 a n d  A pple  C ar P la y . \nMan ag ed   a  te am  fo r  deve lo p m en t  of  Apple   co nnectiv it y   fo r  Auto m otiv e\nin fo ta in m en t a n d  m an ag in g  A pple &#039;s  c e rt if ic a tio n p ro ce ss g lo b ally . \nSp earh ead ed   th e  Connectiv it y   dom ain   fo r  sm art   pho ne  in te g ra tio n  w it h\nau to m otiv e  in fo ta in m en t a n d  p ro je ctio n t e ch no lo gie s. \nSp earh ead ed  th e  In fo ta in m en t  sy ste m  an d  re sp onsib le   fo r  han d lin g   m ult ip le\nap plic a tio ns  a n d  re sp onsib le  fo r  d eve lo p m en t  o f  A nd ro id  In fo ta in m en t  S yste m\nwhic h  in clu d es w ork in g  w it h  w ir e le ss p ro to co ls . \nHan d le d  a  t e am  fo r  d esig n a n d  d eve lo p m en t fo r  S m art  P ho ne C onnectiv it y  a n d\nPro je ctio n t e ch no lo gie s in  C onnecte d  C ars . \nHav e  g o od  u nd ers ta n d in g  o f  iA P1,  IA P2,  A O SP  A nd ro id  C ar  P la y  C ontro l  B onjo ur\nse rv ic e  a n d  p erf o rm an ce  t u nin g  o f A pple  C ar P la y  s o lu tio n f o r M Fi  c e rt if ic a tio n. \nSK IL LS  /  T O OLS  :  \nAnd ro id  A pplic a tio n D eve lo p m en t \nCore  J a v a \nGen eric  F ra m ew ork  \nHA L L a y e r \nJa v a 8  \nC P ro gra m min g  L a n g uag e \nC++ \nAnd ro id  T e le p ho ny \nAUTO SA R \nIn fo ta in m en t H ard w are  T e stin g  \nSo narc u b e \nJIR A  \nMVVM  \nIn stru m en ta tio n T e stin g  (  U I T e stin g  )  \nRo om  D ata b ase  \nPerf o rm an ce  T e stin g  \nAD AS \nJIR A  X -R ay  \nGIT H U B \nKotlin  \nKotlin  C oro utin es \nLIN UX \nAnd ro id  S D K \nNav ig atio n C om ponen t \nAuto m otiv e  I n fo ta in m en t \nAO SP  \nAnd ro id  A uto  P ro je ctio n \nApple  C ar P la y  \nTh ir d  P art y  L ib ra rie s &amp;  A PI&#039;s  \nMult im ed ia  I n fo ta in m en t \nFig m a(U I/ U X) \nFlu tte r \nGoogle  P la y  S to re  \nDart  P ro gra m min g  \nBin d er I P C  \nApplic a tio n S ecu rit y  \nSA ST /  D AST \nAnd ro id  F ra m ew ork  \nAnd ro id  A pp D eve lo p m en t \nAID L /  H ID L \nUnit  T e stin g  \nPen etra tio n T e stin g  \nTe le m atic s \nPro je ctio n T e ch no lo gy \nConnectiv it y  \nGIT  \nGIT H U B \nCA N oe \nVeh ic le  S p y \nRA D -G ig asta r \nPR O JE C TS :  \nCogniz a n t :  G oogle  P la y  P ro te ct (  G .P .P  )\n ( 1 2 /2 0 20  -  0 4/2 0 20 )\n \nCap gem in i  :  H A RM AN  (  T o yo ta  T A S70 0 )\n ( 0 4/2 0 23 -  1 0 /2 0 23)\n \nMah in d ra  &amp;  M ah in d ra  :  M ah in d ra  E le ctric  S U Vs H M I I V I\nApplic a tio ns (  B E.0 5,  B E.0 7,  B E.R A LL-E ,  X U V.e 8 ,  X U V.e 9\n ( 1 0 /2 0 23 -  P re se n t)\n Ach ie ve m en ts /T a sk s \nTa sk s \nhk.v is h w as2 0 23@ gm ail.c o m  \n+91 :  7 9 75 35 0 95 8  \nBan g alo re ,  I N DIA  \nlin ke d in .c o m /in /v is h w as-h -k -0 610 4616 6 \nVIS H W AS H  K  \nAnd ro id  A uto m otiv e  H M I / F ra m ew ork / I n fo ta in m en t\nDeve lo p er \nAn e n th usia stic  M ob ile  A pplic a tio n D eve lo p er  (  S o ft w are  E n g in eer  L e ad  )  w it h  a\nro b ust  A nd ro id  Secu rit y   &amp;  p ro gra m min g  exp osu re   h av in g  clo se   to  4  ye ars   o f\nexp erie n ce  in  A nd ro id  H M I/ S D K,  R eve rs e  e n g in eerin g  w it h  h an d s o n e xp ert is e  in\nko tlin ,  J a v a,  F lu tte r,  A nd ro id  S tu d io ,  G IT ,  J IR A  a n d  A uto m otiv e  P ro je ctio n D om ain\nConnectiv it y .  C urre n tly  w ork in g  a s a  S en io r E n g in eer in  M ah in d ra  &amp;  M ah in d ra  \nin  t h e\nAuto m otiv e  so ft w are  la n e a s  a  d ed ic a te d  a n d  fo cu se d  H M I  e n g in eer  p rim arily\nfo cu sin g  o n d eve lo p in g  I V I a p plic a tio ns f o r N ext-G en  M ah in d ra  E V  V eh ic le s.\n\nPag e 2  o f 2  \nWORK  E X PER IE N CE :  \nCog niz a n t T ech nolo g y S o lu tio n s (  C TS )  \nTe ch nic a l  L e ad  (  A pplic a tio n D eve lo p m en t &amp;  A nd ro id  S ecu rit y\nLe ad  )  \n12 /2 0 20  -  0 4/2 0 23\n, \n  Ban g alo re  \nFu ll  T im e \nExp ert is e   in   re ve rs e -e n g in eerin g   an d  co d e-le ve l  se cu rit y   au d it in g ,  as  w ell  as\nexp erie n ce  in  h an d lin g  o b fu sc a te d  c o d es o f a n d ro id  a p plic a tio ns. \nGood  E xp osu re  in  c o nd uctin g  S ecu rit y  T e stin g  o f  b oth  S A ST a n d  D AST a n d  to\nsa fe g uard  a p plic a tio n c o d e f ro m  m alw are  s u ch  a s s p yw are ,  p his h in g ,  r a n so m ware ,\ntro ja n s,  b ack d oor,  r o otin g  a n d  o th er c y b er t h re ats . \nPerf o rm in g  in te rn al  a n d  e xte rn al  v u ln era b ilit y  a sse ssm en ts  a n d  p erf o rm in g  d ata\nan aly sis  a n d  c re atin g  d ete ctio n r o utin es f o r d if fe re n t in te rn al  p ip elin es. \nExp erie n ce  in  M alw are  a n aly sis  o n A nd ro id  M ob ile  p la tfo rm ,  id en tif y  t h re ats  a n d\ncla ssif y  t h em  a cco rd in g  t o  t h eir  f a m ily . \nCond ucte d  M alw are  A ud it in g  o f c lo se  t o  1 0 ,0 00 A nd ro id  A pplic a tio ns in  t h e\nMuW S,  P SL t ra ck s a s a  S M E. \nED UCA TIO N :  \nM.T ech  (  U niv ers it y  V is v esv ara y a C olle g e o f E n g in eerin g  )  \nCom pute r S cie n ce  &amp;  E n g in eerin g  \n10 /2 0 18  -  1 2 /2 0 20\n, \n  Ban g alo re  \n8.9 3 C G PA\n  PU BLIC A TIO NS :  \nTru st M an ag em en t f o r S erv ic e -O rie n te d  S Io T S yste m s \nPub lis h er :  A CM  D ig it a l  L ib ra ry  \nhttp s:/ /d oi.o rg /10 .1 14 5/3 4 46999.3 4 476 35  \nTa sk s \nGra d e',2,2,1,'Recruiter','SL','N',14,1,'2024-08-05 01:53:34','2024-08-02 01:53:34',0,NULL,NULL,'2024-08-02 01:50:50',0),(490,117,76,NULL,NULL,'76_117_162646246066b4bd1a5ceae_1_VinayChaudhari_DataScientist_6yr_updated_on_202406_02GenAI_1720595461567_Vinay_Chaudhari_.pdf','1.VinayChaudhari_DataScientist_6yr_updated_on_202406_02GenAI_1720595461567_Vinay Chaudhari .pdf','Vin ay C hau d hari \nData  S cie n tis t \nExp erie n ce d  D ata  S cie n tis t w it h  \n6 y e ars\n i n  \nML,\ngen era tiv e  A I, a n d  p re d ic tiv e  a n aly tic s\n, s e ekin g  t o\nin no va te  i n  \nGen A I p ro je cts\n. \nvin ay ch au d hari1 996@ gm ail .c o m  \n+91 8 484991328 \nPune, I n d ia  \nlin ke d in .c o m /in /v in ay -c h au d hari- a i \nmed iu m .c o m /@ vin ay 1 996 \nWORK  E X PER IE N CE \nData  S cie n tis t \nBrid gesto ne E M EA  \n01/2 023 -  P re se n t\n, \n  (R em ote ) P un e, I n dia  \nGlo bal l e a d er i n  m obil it y  s o lu tio n &amp;  t y re  m an ufa ctu rin g \nRole : \nI d eve lo ped  M L &amp;  a n aly tic s s o lu tio ns f o r i n te rn al s ta ke h o ld ers  a n d\nsu b sid ia rie s a cro ss t h e E M EA  r e g io n. \nLLM -B ase d  C usto m er R evie w  A naly sis\n: I m pro ve d  c u sto m er s a tis fa ctio n b y\nid en tif y in g  c o m mon p ro duct i s su es a n d  a re as f o r i m pro ve m en t. U se d  O pen ai\nGPT-4  m odel &amp;  R AG  o n c u sto m er r e vie w  d ata  \nDem an d  F o re ca stin g : \nDesig ned  a n d  i m ple m en te d  1 0+ u niq ue f o re ca stin g\nmeth o do lo gie s a cro ss E u ro pe, o ptim iz in g  5 000+ p ro duct S K U s a n d  i n te g ra tin g\nLa rg e L a n g uag e M odels  ( L LM s) f o r s c a la b le  s o lu tio ns. \nApplie d  R ese arc h : \nCond ucte d  g ro und bre akin g  r e se arc h  o n m ult i- t im e s e rie s\npre d ic tio n a t s c a le , e n han cin g  c o m puta tio nal e ﬃ cie n cy  b y 8 0%  a n d  i n cre asin g\nfo re ca stin g  a ccu ra cy  b y 2 5% . \nTe ch  s ta ck : \nNeu ra lF o re ca st, S ta ts F o re ca st, L la m aIn dex ,  l la m a, \nHuggin g F a ce\n,\npysp ark , p an das, A zu re  D ata b ric k s, O pen A I\n \nSe n io r D ata  S cie n tis t -  P ro duct D eve lo pm en t \nAays A naly tic s \n01/2 022 -  1 2/2 022\n, \n  (R em ote ) M um bai, I n dia  \nSe a so ned  v e n do r f o r G en A I &amp;  a n aly tic s \nRole :\n I  c re ate d  N LP  a n d  A uto M L p ro duct a n aly tic s s o lu tio ns f o r c o rp o ra te  f o r\nFo rt u ne&#039;s  1 00 c lie n ts  \nZero -s h o t l iv e  t ra n sc rip ts  c la ssiﬁ ca tio n: \nIm ple m en te d  t o pic  m odelin g  u sin g\nHug gin g  F a ce  B A RT-L a rg e-M NLI f o r z e ro -s h o t c la ssiﬁ ca tio n o n L iv e C hat\ntra n sc rip ts , e xtra ctin g  i n sig hts  t o  g uid e b usin ess d ir e ctio n f o r t h e S e n io r\nLe ad ers h ip  T e am  \nUtil iz e d  L D A  m odel \nin  G en sim  t o  i d en tif y  k e y t o pic s f ro m  t h e t ra n sc rip ts  d ata ,\nto  g uid e t h e b usin ess d ir e ctio n f o r t h e p ro duct t e am . \nML P ro duct D eve lo pm en t:\n  Deve lo ped  s c a la b le  M L a rc h it e ctu re  a n d  M LO ps\nwork ﬂ ow s w it h  a u to  m odel s e le ctio n, M Lﬂ ow  t ra ck in g , a n d  C I/ C D  p ip elin e,\nbo ostin g  d ata  s c ie n ce  p erfo rm an ce . \nTe ch  s ta ck : \nHuggin gfa ce , T ra n sfo rm er, \nGen sim , N LT K , P ysp ark , p an das, A zu re\nData b ric k s, M lﬂ ow , A zu re  D evO ps\n \nData  S cie n tis t \nConnectin g D ots  I n fo te ch  \n11/2 019 -  0 1/2 022\n, \n  Pun e, I n dia  \nEnd-t o -e n d C re a tiv e  a n d T e ch  S e rv ic e s H ouse . \nRole : \nCore  c o ntrib uto r f o r d ra g -a n d -d ro p, n o -c o de s o lu tio n |  D eve lo ped  s c a la b le\nML/N LP\n m odel f o r \nre al- t im e t ic k e t r e so lu tio n\n |  C re ate d  \npre d ic tiv e  e n g in e\n f o r\nweb  s e rie s s u cce ss |  B ooste d  e -c o m merc e  s a le s b y 3 0%  w it h  \nre co m men d atio n\nen g in e\n |  D esig ned  u se r-f rie n d ly  \nAuto M L/A uto A I\n c o m po nen ts . \nJr. D ata  S cie n tis t \nMaxg en  T e ch n o lo gie s \n09/2 018 -  0 9/2 019\n, \n  Pun e, I n dia  \nWeb  d eve lo pm en t s e rv ic e s \nRole :\n  In d ep en d en tly  d eve lo ped  M L s o lu tio ns f o r a d -h o c t a sk s a n d  i n te g ra te d\nasp ect-s e n tim en t a n aly sis  f o r H IN GLIS H  m ed ia  c o m men ts\n, e n han cin g  w eb\nse rie s c o nte n t s tra te g ie s. \nSK IL L S  \nPyth o n \nAI S o lu tio ns D eve lo pm en t \nMach in e L e arn in g  \nDeep  L e arn in g  \nData b ric k s \nTim e S e rie s F o re ca stin g  \nNLP  \nLLM  \nRAG  \nMLF lo w  \nAI \nModel D ep lo ym en t \nClo ud  P la tfo rm s ( A W S, A zu re ) \nLa n g ch ain  \nGen A I \nED UCATIO N \nPune U niv e rs it y  \nBE i n  I n fo rm atio n T e ch n o lo gy ( 7 8% ) \n06/2 014 -  0 6/2 018\n, \n \nPun e, I n dia  \nACH IE V EM EN TS \nDeve lo ped  L LM  b ase d  s o lu tio n \nEnh an cin g c o m pu ta tio nal e ﬃ cie n cy  b y 8 0%  a n d i n cre a sin g\nfo re ca stin g a ccu ra cy  b y 2 5% . \nDeve lo ped  E arly  M ed ia  c o nte n t p re d ic tio n\npla tfo rm  \nOTT m ed ia  w eb  s e rie s s u cce ss o r f a il u re  p re d ic tio n b efo re  i t\nla u n ch  \nONLIN E C O URSE S &amp;\nCER TIF IC ATIO NS \nOra cle  C lo ud  I n fra stru ctu re  2 024 G en era tiv e  A I\nCert iﬁ ed  P ro fe ssio nal \nOra cle  \nLLM  :  A pplic a tio n T h ro ug h P ro ductio n \nData b ric k s \nData b ric k s G en era tiv e  A I F u nd am en ta ls  \nData b ric k s \nAW S M L S ch o lo rs h ip  \nUdacit y  \nNeu ra l N etw ork s &amp;  D eep  L e arn in g  \nDeep Le a rn in g.A I \nIN TER EST S \nRead in g  \nWrit in g  \nPla y in g  C hess \nAch ie ve m en ts /T a sk s \nAch ie ve m en ts /T a sk s \nAch ie ve m en ts /T a sk s \nAch ie ve m en ts /T a sk s',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-08-08 12:42:02',0),(491,117,76,NULL,NULL,'76_117_31620570466b4bd1a5dceb_Abhay_Kumar_24_2.pdf','Abhay_Kumar_24_2.pdf','',2,3,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-08-08 12:42:02',0),(492,117,76,NULL,NULL,'76_117_206997324566b4bd1a5deb5_Amit_Jain_Data_Science_Resume_2024.pdf','Amit_Jain_Data_Science_Resume_2024.pdf','Public	 	\n                	 	\n \n \n     	     	 	\n \n \n \n \n \nPROFILE SUMMARY	 	     	        	PROFESSIONAL EXPERIENCE	 	\n 	\n&bull; 	6.5+  YoE	 	in  Data  Science  roles  across 	\nindustries.	 	\n&bull; 	Thorough  understanding  of  Data  Science \ntools, techniques and technologies in Machine \nLearning,	 Deep  Learning,  Natural  Language 	\nProcessing	 	(NLP)	 	Using	 	Python,  R	 	\nprogramming.	 	\n&bull; 	Thorough  understanding  of  algorithms  like \nRegression, 	Classification,	 	Clustering, 	\nForecasting,  Anomaly  Detection,	 Time  series 	\nModelling etc.	 	\n&bull; 	Sound  knowledge  of  deployment  using  Git, \nDocker, Jenkins, 	Kubernetes, OpenShift	. 	\n&bull; 	Sound knowledge of Azure Machine Learning \nStudio,  AWS  (Amazon  Web  Services)  AI	-ML 	\nServices.	 	\n&bull; 	Extract	ed	 usable  data  from  valuable  data 	\nsources	. 	\n&bull; 	Carr	ied	 out  preprocessing  of  structured  and 	\nunstructured data	. 	\n&bull; 	Analyz	ed	 large amounts of information to find 	\npatterns and solutions.	 	\n&bull; 	Developed  prediction  systems  and  machine \nlearning algorithms.	 	\n&bull; 	Propose	d solutions  and  strategies  to  tackle 	\nbusiness challenges.	 	\n 	\n   	\n&bull; 	Total 1	4+ 	YoE	. 	\n&bull; 	6.5+	 YoE	 in Analytics, Machine Learning, Data 	\nScience.	 	\n&bull; 	7.8 YoE	 in Academics.	 	\n 	\n      	DATA SCIENTIST	 	\n&bull; 	Data Scientist at 	Wipro	, Pune Since 	Mar	 202	2 &ndash; 	\nTill date	 	\n&bull; 	Data  Scientist  at  Equations  Work  IT  Services \nPvt. Ltd., Pune Since Sept 2020 	to Feb 2022	 	\n&bull; 	Data Scientist at Cloudmantra, Pune since Mar \n2020 to Sept 2020	 	\n&bull; 	Data Scientist at Open Insights, Pune since Aug \n2019 to Feb 2020.	 	\n&bull; 	Data  Scientist  at  TorcAI  Digital  Media  Private \nLimited, Pune since Feb 2019 to Jul 2019	 	\n&bull; 	Data  Analyst  at  Findability  Sciences, \nAurangabad since Jan 2018 to Jan 2019.	 	\n       	 	\n      	ACADEMICS	 	\n&bull; 	7.8+  YoE	 Teaching 	experience  as  assistant 	\nprofessor  in  engineering  colleges  since  Aug \n2009 to Mar 2017	 	\n 	\nEDUCATIONAL QUALIFICATION	 	     	PROGRAMMING SKILLS &amp; TOOLS	 	\n 	\n&bull; 	PGP	-2018	 	in  Data  Science,  Business 	\nAnalytics &amp; Big Data in association with IBM	 	\nfrom  Aegis  School  o	f Data  Science	, Cyber	 	\nSecurity	 &amp; Tele	communicati	on	,  Mumbai 	\n(M.H.)	 	\n&bull; 	M.Tech.	-2014	 from  university:  B.U.  Bhopal 	\n(M.P.) with 73%	 	\n&bull; 	PGDIT	-2008	 	from  Indian  Institute  of 	\nTechnology,  Kharagpur  (W.B.)  with  6.67 \nCGPA.	 	\n&bull; 	B.E.	-2006	 from  University:  R.G.P.V.  Bhopal 	\n(M.P.) with 73.16%	 	\n 	\n&bull; 	Programming Languages:	 Python	, R  	\n&bull; 	Software Tools/Technologies:	 Machine Learning, 	\nDeep  Learning,  Natural  Language  Processing \n(NLP), AWS 	AI-ML, Azure 	 Machine Learning	 	\n&bull; 	Databases:	 MySQL, PostgreSQL	, Db2	 	\n&bull; 	Operating System:	 Windows, Linux/Unix	 	\n&bull; 	Authoring Tools:	 Microsoft Office, Open Office	 	\n&bull; 	BI Tools/ Data Reporting 	Tool:	 Qlik Sense	 	\n&bull; 	Version Control:	 GIT	 	\n&bull; 	Model Deployment Tool:	 Heroku, AWS, Azure	 	\n&bull; 	Cloud platform:	 IBM	, AWS, Azure	, GCP	 	\n&bull; 	Project 	M	anagement 	Too	l: Jira, 	GitHub	 	\n 	\nAmit Jain	 	\nData Scientist	 	\n  amitjain2110@gmail.com	 	\nlinkedin.com/in/amit	-jain	-83292740/	 	\n  youTube/amit	-jain	 	\n  github.com/amitjain2110	 	\n 917	9982256\n\nPublic	 	\nWIPRO	 PROJECTS	: 	\n \nProject Title: 	Space Insights	 	\nProject Profile: 	To Build AI model to predict about Ideal sharing ratio	. 	\nRoles &amp; Responsibility: 	 	\na. Analysis to build a machine learning model for Ideal sharing ratio.	 	\nTools/Technologies used:	 Python, 	IBM Maximo Monitor	 	\n \nProject Title: 	Tririga Building	 Insights	 	\nProject Profile: 	To Build	 AI model for 	TBI.	 	\nRoles &amp; Responsibility: 	 	\n    	a. 	Create Anomaly detection model and other AI models for 	OpenShift	 Platform (OCP)	 	\n    	b.  	Successful transition of project from Kubernetes to 	OpenShift	 Platform	 in IBM Cloud	 	\n    	c.     	PostgreSQL to Db2 migration  	 	\n    	d.  	Installation of modules like IoT, cp4d(WML), 	Monitor etc.	 	\n    	e.  	Responsible to run security Scan (Static, Dynamic, 	white source	, mend, PVR, 	twist lock	) 	\n Tools/Technologies used: 	Python, Machine Learning, IBM Maximo Monitor	, IBM 	Cloud,	 Openshift Cluster,  	 	\n                                            	Kubernetes	 	\n \nEQUATIONS WORK PROJECTS:	 	\n \nProject Title:	 Sales forecasting and Demand Forecasting	 	\nProject Profile:	 To forecast the sales and revenue of e	-commerce company.	 	\nRoles &amp; Responsibility: 	 	\na. Build the model using LSTM Time Series	 Modelling.	 	\nTools/Technologies used:	 Python, Azure 	Machine Learning	 	\n \nProject Title:	 Prediction of Motor Speed &amp; temperature 	 	\nProject Profile:	 To predict the temperature and deployed it using Power platform.	 	\nRoles &amp; Responsibility: 	 	\n    	a.  	End to end model creation &amp; deployment	 	\n    	b.  	Build the model using 	Random Forest	 regression	 and XGBoost 	regression.	 	\n    	c.  	Enabled to run the project in Azure Power Platform.	 	\nTools/Technologies used:	 Python, Azure Machine	 Learning Studio, Power App, Power Automate	 	\n \nProject Title:	 Prediction /Analysis of Horse Race	 	\nProject Profile: 	 	\na. To predict which horse will win the race.	 Also, 	decide	 the position of all the horses.	 	\nRoles &amp; Responsibility: 	 	\na. 	Responsible for prediction 	of result 	using 	Random 	Forest	 classification	, XGBoost	 classification	 and ANN 	    	\nclassification	 model.	 	\n    	b.  	Requirement collection from client.	 	\n    	c.  	Schedule and planning for project execution. 	 	\nTools/Technologies used:	 Python, Machine 	learning	 	\n \nProject Title:	 Text summarization of audit files	 	\nProject Profile:	 To read text from 	audit 	reports	(pdf	) and	 summari	ze	 text	 report	. 	\nRoles &amp; Responsibility: 	 	\n    	a. 	Responsible for text extraction	 using AWS Textract 	and summarization	 using BERT	 library	. 	\n    	b.  	 Requirement collection and deployment.	 	\nTools/Technologies used:	 Python, NLP, A	mazon	 Machine learning, AWS Textract, 	Amazon S3, 	Git, Docker, 	  	\n       	Jenkins\n\nPublic	 	\nCLOUDMATRA	 PROJECTS	: 	\n \nProject Title:	 Specific text extraction of audit reports 	 	\nProject Profile	: To extract specific text and its 	related	 data with units from audit reports and publish &amp; analyze	. 	\nRoles &amp; Responsibility: 	 	\n   a. 	Responsible for text extraction using AWS	 Textract	. 	\n   b. 	Discussion with project planning team	 	\nTools/Technologies used:	 Python, NLP, A	mazon 	Machine 	Learning	, A	mazon	 SageMaker, AWS Textract, A	mazon	   	\n       	Comprehend	 	\n \nOPEN INSIGHTS	 PROJECTS	: 	\n \nProject Title:	 BIFE 	- Banking Intension Fulfillment	 Engine (CLTV Module)	 	\nProject Profile:	 To predict customer lifetime value,	 the amount of revenues that you 	expect to generate	 from a 	  	\n  customer during the period over which	 your service will be of value. 	 	\nRoles &amp; Responsibility:	 	\n    	a. 	Responsible for developing software.	 	\n    	b. 	Wrote down test cases for checking the	 functionality of various 	functions.	 	\nTools/Technologies used:	 Python, Machine 	learning	 	\n \nTORCAI	 PROJECTS	: 	\n \nProject Title:	 Zirca Campaign	 	\nProject Profile:	 To collect advertiser campaign	 information based on 	distinctive features	 performance to predict 	  	\n                         	and	 recommend	 blacklist &amp; whitelist values.	 	\nRoles &amp; Responsibility: 	 	\na. Responsible for collecting	 data 	using Spark 	and analysis	 data using keywords and 	based on	 result create 	\nblacklisted and whitelisted features	. 	\nTools/Technologies used	: Spark, Scala	 	\n \nFINDABILITY SCIENCES	 PROJECTS	: 	\n \nProject Title:	 Ajinomoto Price prediction	 	\nProject Profile:	  To predict the price of product	 after every week, after 1 month, 	after 2 months and after 3 	  	\n                           	months	 value.	 	\nRoles &amp; Responsibility: 	 	\na. Build the model using 	Linear, 	Random Forest	 regression 	and XGBoost regression.	 	\nb. Responsible for EDA, deliver insights and	 prediction result.	 	\nTools/Technologies used:	 R - Programming	, Machine learning	 	\n \nProject Title:	 Net promotor score 	 	\nProject Profile:	 Classification of user sentiment for various telecom plans. 	 	\nRoles &amp; Responsibility: 	 	\na. 	Build the model using 	Logistics regression, 	Random Forest	 classification	 and 	Na&iuml;ve Bayes	 classification.	 	\nb. 	Responsible for predicting classification report.	 	\nc. 	Responsible for EDA, deliver insights and prediction result.	 	\nTools/Technologies used:	 R - Programming	, Machine 	Learning',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-08-08 12:42:02',0),(493,117,76,NULL,NULL,'76_117_156178935966b4bd1a5e0c2_CV_Architect__Amol_Naik.pdf','CV_Architect__Amol_Naik.pdf','Resume of Amol	 	 	Page 	1 of 	9 	\n                    	                    	 	            	       	 	              	\n 	\n 	\nAMOL	 HIRAMA	N NAIK	 	\nFLAT NO 	403	 BLDG	 TULIP 	ADITYA 	GARDEN CITY 	PHASE 	5  PUNE CITY 	PUNE 	(CB)	  411058	 	\n Mob: 	9356997358 	Email	: amolhiramannaik	@gmail	.com	 	\n \nEXECUTIVE 	PROFILE	. 	\n 	Around	 17 years of 	experience in	  Designing 	Web,	 Win	dow 	and Cloud Native 	Applications	. 	\n 	7+  years of  experience in  Solution architecture  and 	developing 	End  to  End 	complex  Single	-Page  Responsiv	e 	\nWeb Applications	 	\n 	7+ years of experience in designing, implementation a	nd architecting the cloud solution	-AWS,GCP,Azure	 	\n 	6+  years  of  experience  using  AWS,  ideally including  comfort  with  the  majority  of  the  following  services  (or \nequivalent):  EC2,  S3,  CloudFront,  Lambda,  IAM,  Route  53,  ECR,  ECS, 	EKS,	CodeBuild,  CodeDeploy, 	\nCodeP	ipeline, SNS, CloudWatch	,Datadog,AWS Neptune,AWS Control Tower	 	\n 	7+ years of rich experience in 	UI 	Architect	ing Scalable, Secure 	Web solutions	. 	\n 	9+ years 	of  experience  as  Full  stack  developer  in  building  web  App 	UI  Design, 	Micro  Frontends  using 	\nAngular2/4	/6	/8	/10	,AngularMaterial,	JavaScript	,HTML	5,ReactJS,	MongoDB,	Cassandra,	Microservices	,Spring	\nBoot,NestJS	,.Net Core 3.0	 	\n 	5+ years of client 	facing 	experience worked	 on 	cross	-functional	 teams for resolving various functional issues 	\nand gathering appropriate requirement	s.  	\n 	3+ years of working experience on Developing Window Applications using WPF with MVVM	 Pattern	 	\n 	Support	ed 	the  business  development  lifecycle  (Business  Development,  Capture,  Solution  Architect,  Pricing 	\nand Proposal Development)	 	\n 	Strong  knowledge  of  Amazon  K	inesis,  AWS  Lambda,  Amazon  Simple  Queue  Service  (Amazon  SQS), 	\nAmazon Simple Notification Service (A	mazon SNS),EC2,S	ssss	3,Bucket,ALB.	 	\n 	Strong knowledge with Web Services, API Gateways and application integration development and design	. 	\n 	 GCP Knowledge of Clou	d Function,Google Kubernetes Engine(GKE),Cloud Pub/Sub,Cloud Load Balancing	 	\n 	Skilled 	in Code Optimization, Review,	 Perfor	mance Tuning for web application	 and SQL	 server 	 Database	 	\n 	 Implementation  of  design  patterns  such  as  Singleton, 	Factory,  Abstract	 Factor	y,  Command	,  Adapter	,  	\nObserver 	in .Net	 	\n 	 Implementation of Software Design Principle, SOLID Principles	 	\n 	Worked on Dependency Injection for code refactoring and loose coupling	 	\n 	 Extensively used core 	.Net technologies like 	LINQ, SOAP Web Services, WCF	 	\n 	 Extensi	vely used Microsoft Tools like Enterprise Library, Entity Framework, etc	 	\n 	 Experience in writing Reporting 	Services	 Rendering, Delivery and Data Source Extensions	 	\n 	 Experience  in  providing  Proposals,  Estimations  (Task  Ba	sed  and  Function  Point)  for  New 	Develo	pment, 	 	\nReengineering and Sustained Engineering projects	 	\n 	 Well  versed  with  complete  Software  Development  Life  Cycle  (SDLC)  process  which  includes	 Design, 	\nConstruction, Testing and Deployment	 	\n 	 Created UI Library using	 Angular Material and Storybook	 componen	t Library.	 	\n 	Proficient 	knowledge in SSO,	 Saml,	 OpenIDConnect, OAuth 2.0	 	\n 	Experienced in working on RFP	 and 	Presales	 	\n 	Worked  with  DevOps  tools  Jenkins  for  Continuous  Integ	ration, 	Deployment,  JIRA	 for  project  Management 	\nand Confluence for 	keeping project	 relate	d documents.	 	\n 	Worked with testing tools framework Jasmine, Karma and Protractor	 	\n 	Worked with 	IIS and .Net Assemblies	 during application deployment.	 	\n 	Profound knowledge in working with 	RD	BMS includes MS SQL Server 2012	\\2008	, Oracle	11g	 	\n 	Excellent skills in writi	ng 	stored 	procedures, T	-SQL 	Queries, 	functions, triggers, views	 and schemas.	 	\n 	Good Experience with Unified Modeling Language(UML) standards and documentation and experience	 	\nIn preparing 	Class 	Diagrams, Sequence	 Diagrams using	 design tools 	such as Microsoft 	Visio.\n\nResume of Amol	 	 	Page 	2 of 	9 	\n 	Performed  Unit  Testing  using  tool  NUnit 	and	 performed  several  integration 	testing,  System	 testing  and 	\nperformance testing.	 	\n 	Strong  analytical  and  dynamic  trouble  shooting  skills coupled  with  excellent  communication  skills  and  able \nto work individuall	y and as a team.	 	\n 	Basic knowledge of Generative AI(Currently attending classes on Generative AI with Great Learning)	 	\nCERTIFICATION	 	\n 	\n 	TOGAF 9(Combined Level 1 &amp; 2) Certified Professional	 	\n 	Google Professional Cloud Architect	 	\n 	AWS  Certified Solutions Architect	-Associate	 	\n 	Microsoft Certified: Azure Solution Architect Expert	 	\n 	SAFe 4.0 Agile Certified Professional	 	\n 	Microsoft Certified: Azure AI 900 Fundamentals	 	\n 	Generative AI for Business with MSFT Azure Open AI Program	 	\n 	\nORGANIZATIONAL EX	PERIENCE	 	\n6 Jan 2022 to till date	          	T-Systems as	 Senior Technical Architect.	 	\nApril 2021	 to 	4 Jan 2022	     	HCL Technologies 	as Senior Technical Architect	 	\nMay 2019 to 29 Jan 	2021 	   Western	 Union	 Technology 	Engineering as	 Solution Architect.	 	\nMay 17 to 	April 2019	       	  Infosys, Pune	 as Technology Architect	 	\nNov&rsquo;11 	 - May 17	 	Tata 	Consultancy	 Services, Pune as Assistant Consultant	 	\nSep&rsquo;10 	&ndash; Nov&rsquo;11	  	Syntel Ltd., Pune as Analyst Programmer	 	\nJul&rsquo;08 	&ndash; Aug&rsquo;10	  	Acidaes Solutions Pvt. Ltd., Mumbai as Engineer 	- Application Development Group	 	\nJan&rsquo;	08 	&ndash; Jul&rsquo;08	 	 	Orion India Systems Pvt. Ltd., Mumbai as Software Developer	 	\nApr&rsquo;07 	&ndash; Jan&rsquo;08	  	IL &amp; FS Education &amp; Technolo	gy, Mumbai as Software Developer	 	\n \n \nCAREER 	FOCUS	 	\n 	Enterprise  application  |  Solution  Architect  |  Cloud  Migration  |Digital  Transformation  |  M	icroservices  | 	\nStrategic  Planning &amp;  Execution  |  Leadership  &amp; Team  Building  |Customer  Relationship  | Generative  AI  for \nBusiness	 	\nTECHNICAL 	SKILL 	SET 	\n \nProgramming Languages	 	C#,JAVA, Typescript	 	\nJavaScript  Framework  &amp; \nLibrary	 	\nAngular	 14, NodeJS	,Javascript, Boo	tstrap	,ReactJS,Angular	Material,	 	\nStorybook UI Component 	library, Web Component	,SpringBoot	,NestJS	 	\nDatabase	 &amp; No SQL DB	 	SQL Server 2012	\\2008	,Oracle11g	,MongoDB	,Casandara	 	\nMiddleware	 	Microservices	,Kafka	,RabbitMQ	,Springboot	,Python	,Micronaut	 	\nBuild  Packaging  Too	l 	and 	\nFramework	 	WIX 3.7	,MSBuild	,Angular CLI,Gulp	 	\nModeling 	 and Design Tools	 	UML,MS Visio	,Enterprise Architect	 	\nTestingTools Management and \nFramework	 	NUnit	,Jasmine,Karma,Protractor	,SonarQube,Resharper,HP ALM	 	\nVersion Control Tools	 	MS Visual Source Safe(VSS	),SVN,TFS	,Perforce,JIRA	,Github	 	\nDimension	 	\nCloud 	 Computing	 	\nAWS	EC2,RDS,S3,Glacier,SQS,SNS,VPC,CloudFormation,IAM,Route53	\n,EBS,Lambda,Kinesis,API  Gateway,ALB	 GCP	-Cloud  Function,Google 	\nKubernetes  Engine(GKE)  Cloud  Pub/Sub,Cloud  Load \nBalancing.	Azure	 Event Hubs,	API Management,Storage,Queues,	 	\nSecurity	,AWS Datadog,AWS Neptune,AWS Control Tower	 	\nOperating System	 	Win 95/98/NT/XP	 /Win 2003	/Win 2007	 	\nDevOps 	 	Jenkins, 	JIRA	, Docker	, Kubernetes	,OpenStack\n\nResume of Amol	 	 	Page 	3 of 	9 	\n \n \n \nPROJECTS 	EXECUTED 	 	 	\n \nT-Systems as Senior Technical Architect	 	\nDuration: January 2022 to Till Date	 	\nProject: LogisticBus: The LogisticBus is a system used 	Daimler	 for supplying parts of vehicles to Retailer.	 	\nLogisticBus  systems  consists  of  Modules  like  Dealer  Inventory  Management  Systems,  Stock  Information  and \nControll	ing 	Systems	. When	 retailer, systems, places the order	 checks if the part is availab	le with wholesaler.	 	\nAbout  90%  parts  ordered  by  retailer  are  available  with  corresponding  wholesaler.  This  is  possible  due  to \napplication such as systems demands and Forecast	 applications. But if it is not available with wholesaler then the 	\nrequest goes to central warehouse and retailer is informed after how many days his demand will be fulfilled	 	\n 	Responsible for delivering architecture guidelines for LogisticBus Modernization 	Application.	 	\n 	Le	d development team in 	Modernization	/Transformation 	LogisticBus	 legacy Application.	 	\n 	Involved 	in 	the 	requirement gathering m	eetings with Product owner. 	 	\n 	Created the Micro services Orchestration layer for 	retailer order, Transferring order deta	ils to other systems.	 	\n 	Worked with the UX designer in designing the wireframes like lending page with the main components.	 	\n 	Design, both technically compliant and secure Cloud solutions, as well as value	-based, on	-demand services to 	\nfacilitate the effective 	transition and migration of projects and p	rograms into 	unique 	cloud	 environment	. 	\n 	Design and implemented AWS Simple S	torage Services to store sales inventory information.	 	\n 	Worked with the EC2 instances to host the 	Micro services	 and the Application.	 	\n 	Involved	 in creating the VPC used cloud Formation Template	 	\n 	Used the Hystrix Library to implement the circuit pattern for 	Micro services	. 	\n 	Implemented the AWS API Gateway for 	Authorization, caching	 and storing the  REST API  Response.	 	\n 	Involved in exploring the cloud	 agnostic architecture AWS,Azure and GCP	 	\n 	Involved 	in Deploying	, Monitoring and Hosting of the 	Micro service	. 	\n 	Used Angular Material	 to design	 and build up a multi 	layered System	. 	\n 	Used Sonar Qube to 	monitor	 the Code Quality Metrices.	 	\n 	Involved in code review 	and approve the Pull Request	 	\n 	Led the deployment of  the code builds and helping in continuous integration and deployment.	 	\n 	Used JIRA, Remedy to create user stories ,is	sues, plan.	 	\n 	Involved in creating the Database Model	. 	\nEnvironment: 	Visual Studio Code, 	AWS 	Cloud.	Angular	 12, Typescript,HTML5, 	\nCSS,JIRA,,AngularC	LI,Angular Material PostgreSQL,	Kafka,Amazon Web 	\nservices,Docker,Gitlab,NodeJS,Java,SpringBoot	,Kubernetes,	IntellijIDEA,	 	\n \nHCL Technologies as Senior Technical Architect	 	\nDuration	 April 2021	 to January	 2022	 	\nProject:  Working on a project to 	enhance	 secured password system for a US based client	. 	\nEnvironment: 	C#	, Web	 Services	, WPF, SQL	 Server	 	\n \nWestern 	Union, Pune	 	\nDuration: 	May 2019	 to 29 Jan 2021	 	\nProject:	 iWatch	,RTRA,Transvision  applications  are  involved  in 	case  management  application  that	 provides 	\ncompliance officers, 	investigators and  analysts  the  ability  to  create 	and/or  review information  about	 a  subject  or 	\ngroup of subjects for the purposes	 of investigation, assessment, 	and intelligence maintenance. iWatch	 was crea	ted 	\nin response to requirements 	from the Global Compliance organization and is intended to serve	 as a single &ldquo;source 	\nof truth&rdquo; 	for compliance activities globally includin	g those compliance user groups.	An iWatch case can be for any 	\nsubject such as 	an Agent, a consumer, a geography or a group/network of Agents and/or Consumers.	 	\nRTRA is a Real Time Risk Analysis application provides the details about the 	customers	. 	\nTransvision  is  a  read  only  web 	application  used	 by  the  Agents  to  monitor 	the  suspicious	 activity  on  the 	\ntransactions.	 	\n \nResponsibilities:	 	\n 	Responsible 	for delivering architecture	 guidelines	 for iWatch Modernization Application.	 	\n 	Le	d development	 team in 	Modernization	/Transformation iWatch	 legacy Application.	 	\n 	Involved 	in 	the 	requirement gathering	 meetings with 	Product owner	.  	\n 	Created the 	Micro services Orchestration	 layer for Case creation, Case bulk load, Case push ,Case search, 	\nCase Integration with other application.	 	\n 	Worked with the UX designer in designing the wireframes like lending page with	 the main components	.\n\nResume of Amol	 	 	Page 	4 of 	9 	\n 	Design, both technically compliant and secure Cloud solutions, as well as value	-based, on	-demand services to 	\nfacilitate the effective transition and migration of projects and p	rograms into unique 	 cloud environment	. 	\n 	Design	 and impleme	nted AWS Simple Storage Services to store and retrieve user specific kyc document	 	\n 	Worked with the EC2 instances to host 	the Microservices	 and the Application.	 	\n 	Involved in creating the VPC used cloud Formation Template	 	\n 	Used the Hystrix Library to implement 	the circuit pattern for Microservices.	 	\n 	Implemented the AWS API Gateway for Authorization,caching and storing the  REST API  Response.	 	\n 	Involved in exploring the cloud 	agnostic architecture 	AWS	,Azure and GCP	 	\n 	Involved in  Deploying, Monitoring and Hosting of 	the Microservice.	 	\n 	Used Angular Material	 to  design and build up a multi layered  System.	 	\n 	Used Sonar Qube to monitored the Code Quality Metrices.	 	\n 	Involved in code review and approve the Pull Request	 	\n 	Led the deployment of  the code builds and helping in cont	inuous integration and deployment.	 	\n 	Used JIRA, Remedy to create user stories ,is	sues, plan	. 	\n 	Involved in creating the Database Model	 	\n 	Created and maintained the confluence page for all project specific details like Project Architecture etc	 	\n 	Involved in the PI 	planning to select the MBI and grooming the team on same.	 	\nEnvironment: 	Visual Studio Code, Angular	 8, Typescript,	HTML5, CSS,JIRA,,AngularC	LI,Angular Material 	\n,Cassandra,Kafka,Ama	zon Web s	ervices,Docker,Gitlab,NodeJS,	Java,	SpringBoot	,Kubernetes,AWS	 &amp; 	\nGCP	,SSO	,ASP.Net core 3.0	 	\n 	\nInfosys Ltd,Pune	 	\nClient: E.ON UK	(Worked at client location  Nottingham,UK from June 18 to Feb 19)	 	\nDuration: May  2018 to April 2019.	 	\nGSOS	-Guaranteed Standards of Service: These are the standards set by the E.ON and some of these are part	 of the 	\nobligations  set  for  energy  companies  by  Ofgem,  the  energy  industry  regulator.  Some  are  standards  that  the \nelectricity  distributors  and  gas  transporters,  who  make  up  the  distribution  network,  have  to  meet.  If  these \nstandards  doesn&#039;t  meet  then  electr	icity  distributor  or  gas transporter 	- will  pay  to  the  customer  subject  to certain 	\nexemption	 	\nResponsibilities:	 	\n 	Led in requirement gathering meetings with business customers.	 	\n 	Technical design including developing the component views of system	 	\n 	Design/Develop	ment of a new .Net Core Microservice to read Servicing data that invoked by the API.	 	\n 	Created MSMQ  to capture the customer transactions and support the developers in implementation of \ndesign wireframes.	 	\n 	Created the service layer which helps all developers/	 feature packs to utilize the APIs t	o be consumed in app	 	\n 	Created standardized API designing such that every  supplier uses its own RES	Tful API design	 	\n 	Involved in  Deploying, Monitoring and Hosting of the Microservice.	 	\n 	Used Angular Material	 to  design and b	uild up a multi layered  System.	 	\n 	Led the deployment of  the code builds and helping in continuous integration and deployment.	 	\n 	Used JIRA, Remedy to create user stories ,issues, plan sprints and to distribute tasks amongst team members.	 	\n 	Worked on the Advance	d queue on high volume transactional systems in Oracle.	 	\n 	Involved in DB Performance Optimization as well.	 	\nEnvironment: 	Visual Studio Code, Angular	4/6	, HTML5, CSS,JIRA,,	NodeJS,AWS,	AngularCLI,Angular 	\nMaterial ,Oracle 11g.	Azure	,ASP.Net core 2.0	 	\nInfosys Ltd,Pun	e 	\nClient: HSBC	 	\nDuration: May 	 2017 to March 2018	 	\nStaff  Frond  End  :  The  SFE  is  an  web  application  used  by  the  front  end  bank  employees  to  get/view  all  the \ncustomer related information like Customer overview, Customer Family ,Loan Repayment schedule and Loan	 	\nRepayment Holiday etc. 	 	\nResponsibilities:	 	\n 	Development and technology planni	ng and implementation 	&ndash; providing	 guidance in best practice and tool 	\nuse in the design and build of technical frameworks and the applications built on them and own and \nimplement co	mplex area of the UI systems.	 	\n 	Architecture and design 	- proposes &amp; implements technical solutions, ensures their integrity, and that the 	\nsolutions can be built, implemented and maintained within timelines/budgets.	 	\n 	Technical design authority 	- internally re	cognized as a specialist in complex technical areas, will oversee 	\nbenchmarking, installation, upgrade, configuration, deployment, and testing activity.\n\nResume of Amol	 	 	Page 	5 of 	9 	\n 	Improves quality 	- investigates and prototypes innovative approaches to improve software quality.	Provide	 	\nStability 	- ensures development teams can develop in a stable and productive environment, with minimal 	\nrework required due to architectural change or related problems as products evolve.	 	\n 	Assisting Project Management in planning technical aspects of projec	ts. 	\n 	Providing regular updates on project status and risks.	 	\n 	Supporting and mentoring individual developers and ensure the team is positive &amp; motivated.	 	\n 	Performing detailed technical reviews to ensure the overall design is being implemented.	 	\n 	Full stack	 Web F	ramework development experience.	 	\n 	Demonstrable detailed understanding of architecture principles and methods, technology, and standards.	 	\n 	Utilizing 	research	 and self	-learning to ensure technical credibility.	 	\n 	Work	ing	 with UI Designer to provide feasibility di	rections and lead UI development team.	 	\n 	Possess	ing 	good	 analytical, communication &amp; interpersonal skill.	 	\n 	Hands on experience in responsive designs  (Bootstrap, Telerik or any other framework)	 	\n 	Participated in meetings with Business users and with other proje	ct means to ensure that the volatile	 	\n business requirements reflecting the project dev	elopment.	 	\n 	Contributing 	to the implementation of User Stories	 	\n 	Participating 	in SCRUM process	 	\n 	Fulfilling 	the acceptance criteria with the contributed code 	(quality, unit te	sts e	tc.)	 	\n 	Adherence to architecture, design and quality guidelines	 	\n 	Maintenance, problem analysis and bug fixing within the 	application	 	\n 	Technical clarifications with other team members	 	\nEnvirionment: 	Visual 	Studio Code, 	\nAngular	,HTML5,CSS,GitHub	,JIRA	,Jenkins,	Protractor,	NodeJS,AWS,	AngularCLI	,OAuth2.0	, ,ASP.Net core 2.0	 	\n 	\nTata Consultancy Services,	 Pune	 	\nClient:NBS Bank	 	\nDuration	:Nov  2015 to March 2017	(Worked at client location Swindon,UK	 from 	Nov 2015 to Nov 2016	) 	\nThe  Nucleus  is  a  web  application that  will  enable	 the  user  to  carry out  account  and customer  related  back  office 	\nservices on the accounts stored in SAP.Nucleus consists of a number of back office services which can be invoked  \neither  from  the  Nucleus  application  or  externally	 from  other  applications  by  p	assing  appropriate  data  to 	\nNucleus.The  external  applications  can  either  invoke  the  Nucleus  application  as  a  whole  or  a  specific  Nucleus \nprocess.In  both  the  cases  the  external  application  needs  to  pass  appropriate  data  confirming  to    a  pre 	defined 	\ninterface	.Nucleus  also  contains  links  to  other  many 	applications.  The	 data  that  is  available  within  Nucleus	 is 	\npassed to 	these external systems	 using the interface.	 	\nResponsibilities:	 	\n 	Develops software solutions by studying information needs; conferring with users; 	Studying systems flow, 	\ndata usage, and work processes; investigating problem areas; Following the software development lifecycle.	 	\n 	Develop features across multiple subsystems within our applications, including collaboration in requirements \ndefinition, proto	typing, design, coding, testing and deployment.	 	\n 	Documents and demonstrates solutions by developing documentation (HLD, LLD), flowcharts, layouts, \nssss	diagrams, charts, code comments and clear code.	 	\n 	Design,  develop  and  maintain  the  Database  Schema,  Entity  r	elationship  diagrams,  Data  modeling,  Tables, 	\nStored procedures, Functions and Triggers, Constraints, Indexes, Schemas, Functions, Views, Rules, Defaults \nand complex SQL statements. 	 	\n 	Maintain a technical understanding of a component or sub	-component within 	an application / product, 	\nproviding support for technical queries, live incidents and problems. 	 	\n 	To ensure that the technical integrity and quality of the applications / products is maintained across their \nlifetime, contributing to the development of any t	echnical changes being proposed. 	 	\n 	To build knowledge of how technical strategies are defined to provide business solutions.	 	\n 	Review Technical Feasibility Studies for projects that impact applications / products within domain, and \nassess potential impact.	 	\n 	So	lve software	-related problems, providing analysis and recommendations as appropriate. 	 	\n 	Develop an awareness of new tools, processes and technical developments relevant to component area of \nexpertise.	 	\n 	Understand emerging software development methods, tools 	and standards. Look for ways to apply as 	\nappropriate	 	\n 	Provide input to end	-to-end system technical designs that involve applications / products within domain, 	\ncollaborating with other internal and supplier teams as required.	 	\n 	Understand the various phases of	 the software development lifecycle.	 	\n 	Undertake impact assessment of change requests against applications / products within domain.\n\nResume of Amol	 	 	Page 	6 of 	9 	\n 	Review test strategies to guarantee the quality of the delivered software assist in the diagnosis of system \nproblems encounte	red during testing. 	 	\n 	Collaborate with other members across teams to build technical solution. 	 	\n 	Define architecture, R&amp;D on new technologies	 	\n 	Make the framework up	-to date based on new technology or new way to implement the software solutions.	 	\n 	Documenting st	andard features, guide to integrate framework features, user manuals, versions etc.	 	\n 	Create deliverables (Packages, Configurations) using IT Strategy approved build tools (Team foundation \nservers) and standards, ensuring they meet all specific install polic	ies defined for the system.	 	\n 	Maintained the source	-code repository, developed multi	-platform product installers and developed scripts 	\nfor builds and source code automation.	 	\n 	Co	-ordination with the Technical Project Managers, Software Developers, Testers, App	lication Support, SAs 	\nand DBAs for any Deployment and Change across environments.	 	\n 	Developed and managed testing and roll	-out project plans for software releases, desktop updates and server 	\nimplementations.	 	\n 	Designing and creating the web services using WCF 	and SQL Server 2010.	 	\n 	Designing the application by using the different design pattern like creation pattern (Factory design pattern, \nMVC, Abstract Factory, and Singleton) and structural pattern (Adapter, Facade etc.).	 	\n 	Complete assignment work to budget, tim	e and quality requirements. Document all work in accordance with 	\nagreed standards, and with re	-use in mind	 	\n 	Involved in Project planning, Resource utilization &amp; Timeline estimations.	 	\n 	Estimating the tasks based on functional requirement and assign the work t	o team	 	\n 	Managed the  offshore and onshore team.	 	\nEnvirionment: 	C#,Visual Studio 2015	, WCF,	SQL Server,,AngularJS,NodeJS	,SAML	 	\n 	\nTata Consultancy Services,Pune	 	\n Client:DNB Bank	 	\nDuration	:Jan2014 to Oct 2015	 	 	\nDescription:	 DLA  (DNB  Loan  Administration)  application  i	s  used  for  Administration  of  Currency  Loan  as 	\nwell  as  tailor  made  loans  in  NOK.  DLA  is  applicable  for  large  corporate 	customer 	and  limited  group  of 	\nprivate customers (Multi Currency Loans only).	 	\n 	Zenit  Workflow  interacts  with  different  rule  engines  and  syst	ems    and  the  offer  letter  to  the  customer   	\ngenerated for the customer mentioning limit and terms and condition. DLA loan is offered to very limited \nspecial customers only, so most of the terms and condition negotiated between loan officer and customer \nout	side the system over the counter.	 	\n 	Large  Corporate:  For  large  corporate  &amp;  International  Customer,  ASAP!LC  takes  care  of  the  Credit \nProcess.  As  the large  corporate  customers involve  high value  loans,  most  of  the  agreement  has  been  set \noutside the box between	 the lawyers of customer and DNB. Details agreement prepared based on various 	\nnegotiation factors like limit, collateral, margins etc. A Credit process can be initiated in ASAP!LC for an \nexisting customer or may be for a new customer.	 	\n 	After  Credit  process 	done  and  finalization  of  the  agreement,  A  Decision  have  been  have  been  taken  to 	\ngrant  the  loan  and  Limit  have  been  decided.  After  negotiation  of  agreement  have  been  over,  a  request \nhave been sent to DLA users through mail to open a loan project in the DLA 	system.	 	\n 	For  a  DLA  Loan collateral is  being  saved  to  Felix  Depot by  the  collateral  department  and  the  Collateral \nDepartment gives the final sign off and send the agreement to DLA department for further processing.	 	\n 	After  receiving  the  credit  decision,  DLA  Ap	plication  users  open  a  new  DLA  loan  project  using  the 	\nCustomer details provided in the agreement. 	 	 	 	 	\n 	After  Credit  process  done  and  finalization  of  the  agreement,  A  Decision  have  been  have  been  taken  to \ngrant  the  loan  and  Limit  have  been  decided.  After  neg	otiation  of  agreement  have  been  over,  a  request 	\nhave been sent to DLA users through mail to open a loan project in the DLA system.	 	\n 	For  a  DLA  Loan collateral is  being  saved  to  Felix  Depot by  the  collateral  department  and  the  Collateral \nDepartment gives the 	final sign off and send the agreement to DLA department for further processing.	 	\n 	After  receiving  the  credit  decision,  DLA  Application  users  open  a  new  DLA  loan  project  using  the \nCustomer details provided in the agreement. 	 	\n 	From DLA client after creation  of 	the DLA  project  there  is  a limit  have  been  entered  for  customer  based 	\non  the  limit  set  in  the  Credit  Process.  This  DLA  limit  is  used  for  reference  and  warning  purpose  only, \nDLA limit will be changed manually based on revision of the limit from Credit Depar	tment	 	\n 	After creation project a Single Loan or Limit	-Tranche Facility have been created through DLA client and 	\nthe amount getting disbursed as per the agreement.	 	\n 	After creation of project and loan it sends information to Felix exposure.	 	\n 	Loan  disbursement  is	 a  manual  process  in  DLA.  DLA  user  need  to  disburse  the  loan  to  the  customer 	\naccount. In case of non DNB customer Disbursement will be done through a internal Global Account.\n\nResume of Amol	 	 	Page 	7 of 	9 	\nResposibilities:	 	\n 	Participated in meetings with Business users and with other pro	ject means to ensure that the volatile	 	\n      	business requirements reflecting the project 	development. Application	 development was developed was 	 	\n      	based on UML methodology.	 	\n 	Involved in the design of the Use Case Diagrams and class diagrams using Micro	soft Visio.	 	\n 	Reworked on a part of the GUI to improve interface without affecting the performance of the application.	 	\n 	Used C# as language to develop code behind business logic	 	\n 	Used C# for middle tier business logic and implementation of Web Services	 	\n 	Followe	d the MVVM design 	pattern, separating	 the 	business, presentation	 and data models.	 	\n 	Performed the conversions of existing VB Applications into WPF application.	 	\n 	Designed the WPF GUI for accessing product 	details, Comparisons	 and Client details.	 	\n 	Developed User	 Interface(UI) using WPF for the better look and feel of the Window based application.	 	\n 	Wrote Stored 	Procedures, Triggers	 and SQL Queries and LINQ in SQL Server 2008.	 	\n 	Developed and consumed WCF Web Service to access data layer components	 	\n 	Involved in Impleme	ntation of Exception handling on User Interface layer.	 	\n 	Created test cases and did basic manual testing.	 	\n 	Used TFS as a central repository and source code control.	 	\n 	Mapping the business requirement to the technical specification and detail design documents.	 	\n. 	\nEnvirionment: 	C#,Visual Studi	o 2012	, WCF,WPF,S	QL Server,Entity 	Framework., Angular	 JS,MVC.	 	\nTata Consultancy Services,Singapore	 	\nClient:Barclays Bank ,Singapore	 	\nDuration	: April 2013 to  Dec 2013.	 	\n Barclays is a major global financial services provider engag	ed in retail and commercial banking, credit cards, 	\ninvestment banking, wealth management and	 investment management services	 	\nDescription:Exposure Analysis &amp; Reporting	-Credit Risk Portal	 	\nExposure, Analysis and Reporting application in Credit Risk Portal is a	n application that supports and 	\nenhances new BASEL 	&ndash; III and SRA requirements. Changes are been introduced in new BASEL III and 	\nStrategic Risk Architecture (SRA) framework, which impact the current reporting architecture. This project \naims to build a syste	m that enable users to perform What	-if calculations and view pre	-calculated exposure 	\nmeasures, apply adjustments for any counterparty, site, product, what	-if trades, actual trades. View 	\nnumerical and graphical display of risk measures. View list of actual 	trades, trade attributes, settlement 	\nprofiles, netting agreements, Collateral arrangements etc for any CSP and Trades.	 	\nPortfolio simulations are required to generate risk measures to manage the counterparty and trading \nbusiness.This can be used to see the 	impact on a portfolio when new derivative and Securities Financing 	\ntrades are entered into by calculating several credit risk measures.	 	\nThe impact on the regulatory capital is also measured by calculating several regulatory metrics.	 	\nResponsibilities:	 	\n 	Respo	nsible for Design and Development of tactical IT solutions for Credit Risk Management System, 	\nactively involved in UI Design, WCF Services, Database Access Layer including Stored Procedures, \nCreating and maintaining Tables, Triggers, Views, Functions, Inde	xes for performance tuning. 	\nInvolvement in SRA Batches and Downstream Feeds for data to be picked up.	 	\n 	Using WPF, WCF, C#.Net 4, Visual Studio 2010, SQL Server 2008, Infragistics Controls, Xceed Grids and \nChart Controls to design and build up a multi layere	d Credit Risk System.	 	\n 	Championed using MVVM and PRISM design approach to ease separation of concerns between UX \ndesign, business logic, service back end and testing layers.	 	\n 	Involved in Performance Optimization at application and database level, Defect Fixe	s, Migration of 	\nExcel to Application and vice versa.	 	\n 	Involved in all aspects of design and development including co	-ordination of testers, BAs and business 	\nusers. Worked with business analysts to determine business rules and feature list.	 	\nEnvirionment: 	C#,	Visual Studio 2010, WCF,WPF,SQL Server,	 Multithreading	 and IIS.	 	\n Syntel. Ltd , Pune	 	\nAMEX 	 	\nClient:American Express 	 	\nDuration:	 Sept 2010  to 18 Nov 2011	 	\nRole:	 Analyst Programmer	 	\nDescription	: 	\n 	Statement  of  Work  (SOW)  Module:Users  can  create  a  request,  fill  out 	the  forms  by  selecting 	\nappropriate  request  type  and  submit  that  SOW  request,  which  notifies  respective  teams  to  take \naction  Once the request process is completed, the status will  be changed to complete      	 	\nand request can be closed. However, request wi	ll be in DB for future reference	.\n\nResume of Amol	 	 	Page 	8 of 	9 	\n 	Resource  Change  Request:Change  can  be  made  for  changing  the  request  by  leader,Requestor  only	 	\ncancel the request.After complete approvation of request,resource allocates to the requestor.	 	\n 	Work  In  Progress(WIP):This  module  i	s  used  to  manage  Project  Estimation  where  transfer  of  hours 	\nfrom projects is managed.	 	\n \n        	Responsibilities:	 	\n 	Involved  in  Requirement  Analysis,designed  and  developed  Web  application  using  C#.Net  4.0  and \nSilverlight.	 	\n 	Gathering the Client Requirements by 	participating in minute	-to-minute meetings and Analysis.	 	\n 	Design the High Level and Low Level Design of the system.	 	\n 	Programming Using Silverlight,C#	 	\n 	Developed  UI  Using  Silverlight  Controls  and  implemented  business  components  and  data  access \ncode using C#.	 	\n 	Involved in the database design and design tables,indexes and views.	 	\n 	Developed Tables,Indexes,Stored Procedures,Database Triggers and Functions.	 	\n 	Implemented WCF Web Services for database connectivity in Silverlight application.	 	\n 	Analyzed and formulated the T	est plan for requirements,created and test scenario from use cases,	 	\ntest case design from design document,regression and test automation using NUnit.	 	\n 	Involved  in  Unit  and  module  level  testing,reviewing  the  requirements  and  fixing  the  defects \nappropriately.	 	\n 	 Involved in detailed documenting of the application that was developed by me.	 	\n 	Provided production support and deployment support .	 	\n 	Project version management and code control and management was done in VSS.	 	\n 	Project was delievered following CMM methodolog	y. 	\n        	Envirionment:	 C#,Visual Studio.Net,Silverlight,WCF,WPF,SQL Server,Nunit and IIS.	 	\n Acidaes Solutions Pvt. Ltd ,Mumbai	 	\nCRMnext	 	\nClient	: HDFC Bank	 	\nDuration:	 Jul 2008 to Aug 2010	 	\nRole:	 Engineer 	- Application Development Group.	 	\nDescription	:  CRMnext  is 	a  business  application  designed  specifically  to  address  companies  wide  ranging 	\nneeds  of  managing  collaboration  and  interactions  between  vendors,  suppliers,  and  customers.  It  helps \ncompanies  efficiently  manage  the  front	-end  processes,  provide  consistency  ac	ross  all  customer  interaction 	\npoints,  and  focus  on  efficiencies,  boosting  bottom  line  revenue  potential.  Implementing  CRMnext  is  a \nstrategic  initiative  for  any  company.  CRMnext  provides  the  technology  platform  required  to  implement  an \nenterprise  CRM    initi	ative.    A  typical  CRM  initiative  is  a  mix  of  strategy,  process  and  technology  CRMnext 	\nenables companies to better manage and seamlessly share information, across departments by centralizing all \ncustomer  data.  It  automates  various  processes  using  its  workfl	ow  capabilities,  provides  powerful  reporting 	\nand  analytical  capabilities  that  deliver  actionable  business  intelligence.  This  enables  CRMnext  to  help \norganizations  create  a  true  360&deg;  view  of  their  own  customers  that  spans  across  departments.  Typically,  an \nend	-to-end  CRMnext  deployment  will  cover  all  customers  facing  departments  and  manage  the  entire 	\ncustomer  lifecycle.  It  covers  the  entire  working  of  the  company  starting  from  lead  generation,  to  closure  of \nsales and from customer service to cross	-sell / up	-sell services. 	 	\n        	Responsibilities:	 	\n 	Involved  in  defining  business  and  system  requirements  and  designed  the  functional  and  technical \nspecifications.	 	\n 	Programming using ASP.Net,C#.	 	\n 	Design and Developed web Forms using ASP.Net,XML.	 	\n 	Used ASP.Net Server Con	trols and HTML controls to design and web pages.	 	\n 	Involved in writing the programming code for web pages using ASP.NET object model.	 	\n 	Used ADO.Net Object Model to Interact with SQL Server Database using C# language.	 	\n 	Used Dataset object and Data Reader object	 to get the data from Database.	 	\n 	Wrote stored procedures and triggers.	 	\n 	Developed  Reporting  and  Analysis  module  by  web  enabling  reports,Web  development  using \nHTML,ASP.Net to deploy reports for the end user to view,print and export data from analysis purposes	. 	\n 	Involved in the development of the test plan Visual Source safe used on source control tool.	 	\n 	\nEnvironment:	 C#, ASP.Net, SQL Server 2005,Javascript,WCF,AJAX  Nunit,Silverlight	 	\n Orion India Systems Pvt. Ltd., Mumbai	 	\nVector 2008	 	\nClient:	 KPMG\n\nResume of Amol	 	 	Page 	9 of 	9 	\nDuration:	 Jan 20	08 to Jul 2008	 	\nRole:	 Software Developer	 	\nPlatform: 	Windows 2003	 	\nDescription	:   This is a custom auditing tool developed for the pioneers in auditing having their footprint all 	\nover the world. The application is developed in .Net  with client component devel	oped in ASP.Net with a 	\nservice running on the server and backend being MS SQL server.  The application is designed to manage all \nthe activities involved in the auditing process. It also uses service oriented architecture for the integration of \nplug in tool	s which allows system integration through loosely coupled components. The system has also got a 	\ncustom made document management system incorporated to it which holds the information in the SQL file \nsystem.	 	\nResponsibilities	   	\n 	Developed web forms using C#,AS	P.Net and SQL Server 2005.	 	\n 	Used Data Binding to bind Servers controls with the parameter from the tables in the database.	 	\n 	Published  Web  Services  using  C#  to  interact  with  the  other  applications  and  exposed  them  using SOAP \nand HTTP.	 	\n 	Designed and Developed A	SP.Net pages including Master and Navigation pages.	 	\n 	Implemented Data Grid view adding,deleting,sorting of rows for all the contacts and addresses of clients.	 	\n 	Involved  in  unit  and  module  level  testing,reviewing  the  requirements  and  fixing  the  defects \napprop	riately.	 	\nEnvironment:	 C#, ASP.Net, SQL Server 2005,Javascript,WCF,AJAX  Nunit.	 	\n 	\n IL &amp; FS Education &amp; Technology, Mumbai	  	\n Assessment Online	 	\nClient:	 ABNAMRO Bank, ICICI Prudential	 	\nDuration: 	April 2007 to Jan 2008	 	\nRole: 	Software Developer.	 	\nDescription	: Assess	ment Online is an online testing engine, which facilitates: 	 	\nCreation of Tests	 - Tests in any domain can be created from a global question bank (owned by organization)	 	\nAdministration of Tests	 - The tests can administer on	-line question bank or any personal	 question bank of a large 	\nof employer 	.The System enables frequent and Effective evaluation of employee and provides feedback about the 	\nTest  to  the  test  administrators  in  terms  of  statistical  analysis  of  questions  and  tests.  The  Assessment  Online  is  a \npowe	rful  Application  suite  of  creating  tests  and  surveys  on  the  web.  AOL  is  designed  to  deliver  online 	\nassessments  that  can  test  not  only  standard  literacy  and  numeric  skills  but  also  avows  organizations  to  easily \ndesign  their  own  questions.  With  AOL  software,	 you  can  quickly  and  easily  create  and  administer  online  tests 	\nwith minimal effort, giving you more time to spend with learners in the classroom or the workplace. 	 	\nResponsibilities:	  	\n 	Developed and Implemented the web page interfaces	 	\n 	Created  ASP.Net  pages  u	sing  C#.Net  for  middle	-tier  processing  of  clients  requests  of  clients  requests 	\nwith ADO.NET interfaces for database and implemented stored procedures.	 	\n 	Used  ADO.NET  and  data  objects  such  as  Data  Adapter,Data  Reader,Dataset,data  table  for  consistent \naccess t	o SQL data sources.	 	\n 	Used SQL Server as RDBMS to create and maintain Tables,Procedures and Views.	 	\n 	Implemented session state management	 	\n 	Migrated the functionality from existing asp pages to  new aspx pages and also added new functionality \nto reflect the Busin	ess users requirements.	 	\nEnvironment:	 C#, ASP.Net, SQL Server 2005,Javascript,Web Services.	 	\n \nEDUCATIONAL 	QUALIFICATIONS	 	\n P.G.D.A.C (Software)	, Y.C.P.A.I.T, A.C.T.S, C.D.A.C Centre, Mumbai,(Feb,2007)	 	\n B.E (	Computer Technology	), K.I.T.S Ramtek, Nagpur,	 (June,20	02)	 	\n 	\nPERSONAL 	DETAILS	 	\n 	Date of Birth	: 1st January 1978	 	\n 	Languages Known	: English, Hindi and Marathi	 	\n 	Passport No:	 W7633055	 	\n 	Marital Status:	 Married',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-08-08 12:42:02',0),(494,117,76,NULL,NULL,'76_117_21561509866b4bd1a5e40d_DIPALI_KSHIRSAGAR_.pdf','DIPALI KSHIRSAGAR .pdf','@\nO\n,\nDIP A LI K A SH IN AT H  K SH IR SA G AR	\nkd ip ali2 001@ gm ail. c o m\n7263 015416\nT han e,  M um bai,  I n d ia	\n	O bje ctiv e\nI s e ek c h alle n g in g  o ppo rtu n it ie s w here  I  c a n  f u lly  u se  m y s kills  f o r t h e s u cce ss o f t h e\no rg an iz a tio n.	\n	E xp erie n ce\nR ohit  A sso cia te  C it ie s a n d  R ails  P vt. L td .\n03/0 7/2 023	 -  	3 1/1 0/2 023	\nIn te rn\nR esp o nsib ilit ie s in clu d e p ro je ct p la n nin g ,  b ud getin g ,  s c h ed ulin g ,  a n d  d eve lo pin g  d ra w in g s\nan d  s p eciﬁ ca tio ns f o r c o nce p t t h ro ug h ﬁ nal  d esig n.\nS ap phir e  D eve lo per&#039;s\n1 4/0 5/2 018	 -  	1 6/0 6/ 2 018	\nIn te rn\nP la n nin g ,  d esig n,  a n d  c o nstr u ctio n o f in fra str u ctu re  p ro je cts ,  g ain in g  p ra ctic a l  e xp erie n ce\nu n d er p ro fe ssio nal  s u p erv is io n. T hey c o ntr ib ute  t o  s it e  in sp ectio ns,  d ra ftin g  p la n s,  a n d\nen su rin g  p ro je ct c o m plia n ce  w it h  s a fe ty  a n d  r e g ula to ry  s ta n d ard s.	\n	E d uca tio n\nS in hg ad  C olle g e O f A rc h it e ctu re ,  P un e\n2 024\nB ach elo r O f A rc h it e ctu re\n6 .5 5\nS an t G aja n an  M ah ara j  R ura l  P oly te ch n ic ,  M ah ag ao n,  K olh ap ur\n2 019\nD ip lo m a in  C iv il  E ng in eerin g\n7 3.5 2\nN uta n  D yn an  M an d ir ,  K aly a n ,  T han e\n2 016\nS eco nd ary  S ch o ol  E d uca tio n\n75.2 0	\n	S kills\nP ro ble m  s o lv in g  s kill\nG ood m an ag em en t a n d  p la n nin g  s kill\nA cce p tin g  m y w ea kn ess a n d  t r y in g  t o  im pro ve\nC urio us t o  le a rn  n ew  t h in g s\n\nAbilit y  t o  c o pe w it h  f a ilu re s a n d  t r y  t o  le a rn  f ro m  t h em	\n	L a n g uag es\nM ara th i\nH in d i\nE ng lis h	\n	T ech n ic a l  S kills\nA uto ca d  2 D  a n d  3 D\nR evit  A rc h it e ctu re &nbsp;\nG oogle  S ke tc h u p &nbsp;\nLu m io n&nbsp;\nP ho to sh o p&nbsp;\nR hin o  ( G ra ssh o pper)\nM S O fﬁ ce &nbsp;',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-08-08 12:42:02',0),(495,117,76,NULL,NULL,'76_117_170264706966b4bd1a5e74a_Dr_Abhijeet_Resume_Industry.pdf','Dr.Abhijeet_Resume_Industry.pdf','Abhijeet	 Thakare	 	\nPhD	 (Computer	 Science)	 	\nORCID	 ID:	 http://orcid.org/0000	-0003	-1096	-6418	 	\n                     	 	\nDOB:	 24th	 May	 1979	 	\n181,	 	Jaidurga	 	Society	 	No	 	1, 	\nNarendra	 Nagar.	 Nagpur,	 India	 	(+91)	-8055044	195	 	\nabhijeetthakarevnit786@gmail.com	 	\nwww.linkedin.com/in/dr	-abhijeet	-thak	 	\nare	-33529618	 	\n                        	 	\n                        	                                                                                                                             	                                                                     	\nEXPERIENCE	 	\nTotal	 Experience:	 19 Years	 	\n \nSoftclouds	 LLC	 &mdash;	 AI	 Ar	chitect	 (R&amp;D)	 	\nSeptember	 2022	 - PRESENT	 	\n \nCurrently working as a 	 AI Architect in the Research and	 	\nDevelopment	 Division	 of Softclouds	 LLC.	 	\n \nPROJECTS	 HANDLED	 	\n● 	 Work	ed	 on Developing Enhanced Search Engine based on	 Question	-	\nAnswering	 System	-Keyword	 and	 Semantic	 Search.	 	\n \nTechnology/Tools:	 	Python	 	3.9,	 	Transformers,	 	BERT,	 	\nSentence	-Transformers,	 Spacy,	 NLTK,	 Dependency	 Parser,	 PyTorch,	 	\nPandas,	 Elasticsearch, OpenSearch	, JSON.	 	\n● 	Developed GEN AI and Predicated	 GEN AI Chatbot.	 	\nTechnology/Tools: 	JSON	,  Retrieval  Augmented 	Generation  (	RAG),  Fine 	\nTuning,  NLP,  LLM&rsquo;s,  Llama  Index,  Semantic  Search  ,  REST  API&rsquo;s,  Vector \nDB, LangChain, Prompt Engineering, NLP	 	\n \n \nShri	 Ramdeobaba	 College	 of	 Engineering	 &amp; Management	 , 	\nNagpur	, &mdash;	 Assistant	 Professor	 	\nJune	 2009	 - September	 2022	 	\n \nPROJECTS	 HANDLED	 	\n● 	Executed	 Successfully	 ﬁrst	 research	 software	 consultancy	 in the	 	\nDepartment	 (in	 2011)	 Implementation	 and	 Enhancement	 	/ 	\nTweaking  of  Synchronization  Protocol  1588v2  over  Multi  Protocol	 	\nLabel	 Switching	 (MPLS)	 Network. 	This  protocol	 enhancement  will	 	\nred	uce   data  loss  by  5%,  red	uce  tim	e  lag  by  15%,  and  red	uce  the 	\nSynch period by	 50% for	 BSNL.	 	\n● 	Developed  and  successfully  deployed	 consultancy  project  &ldquo;  Touch	 	\nScreen  Kiosks&rdquo; 	for  one  of  the 	reputed  Hotel	 clients  in  Panchgani,	 	\nMaharashtra. 	This  deploy	ment 	has  decreased	 the  efforts  of  Hotel	 	\nmanagement  Staff	 in  managing  the  orders  of  customers  by 	100%.(	 	\nTechnology:Java,	 Duration	 : 1 year)	 	\n● 	Developed	 and	 successfully  deployed  an  E	-Commerce  website 	for	 	\none  of  the  startups  in  Nagpur.	 This  has 	increased  their	 business 	in 	\nlockdown	 by	 10%	 in one	 month.	 (  Technology:  Java,Duration:  6	 	\nmonths)	 	\n● 	Successfully 	 completed	 and	 delivered	 software	 development	 project	 	\n \nLANGUAGES	 	\n \nJava,	 Python,	 C, C++,	 JSP,	 	\nServlets,	 	Spring	 	Boot,	 	\nSpring,J2EE	 	\n \n \nSKILLS	 	\n \nMachine 	 Learning,	 	Nat	ural	 	\nLanguage	 Processing,	 Deep	 	\nLearning	 	Transformers,	 	\nQuestion	-Answering	 System,	 	\nSpacy,	 	 Dependency	 	\nParser,	 Generative AI, LLM&rsquo;s, 	\nPrompt Engineering, \nElasticsearch,	 OpenSearch,	 	\nJSON,	 Named	 Entity,	 BERT	 	\nAggregated	 Search,	 Hugging	 	\nFace 	, Data Structures, Obj	ect	 	\nOriented	 Concepts.	 	\n \n \nDATA	BASES	 	\n \nOracle	 , MYSQL,	 WordNet,	 	\nOracle	 Apex.	 	\n \nPRODUCTS	 WORKED	 	\n \nEmatrix,	 Windchill	 	\n \n \nAWARDS	 	\n \n \nNPTEL	- Top	 Performing	 	\nMentor Award (	 	\nProgramming	 in Java)	 in 2021\n\n&ldquo;Development	 of 	Software for Customer Engagement Database 	\n&ldquo; for	 the	 client	 &ldquo;Sanstar	 Microsystems,Nagpur&rdquo;.	 Research	 funds	 	\nreceived	 from	 GIZ,	 Germany	 for	 development	 of this	 project.	 	\n \n \nRESEARCH	 PROJECTS	 	\n1. Project Name:Automatic Extraction of Product Entities and Attributes	 	\nfrom	 World	 Wide	 Web.(	 Status	- Completed)	 	\n \n● Development	 of a  Novel  Search  Engine 	which  automatically  extracts	 	\nentities	 and	 their	 respective	 attribute	 values	 from	 web	 pages	 and	 	\naggregates	 them	 into	 dynamic	 web	-spreadsheets.	 	\n● 100%  time  saving  of  t	he  customer,  as  no  need  to  scroll  results  and	 	\nsearch	 within	 results	 for	 choosing	 the	 right	 product.	 	\n● New  features  added  by  the  manufacturer/retailers  are  also  dynamically	 	\ncaptured	 in the	 web	 spreadsheet.	 	\n \nTechn	olo	gy/Tool	s:    Python,    NLTK    ,Java    1.7.0_80.,   JSOUP	   1.8.2   API, 	\nStanford POS tagger v.  2.0,  Java wor	dNet Interface  API,  Weka,  Mac	hine	 	\nLearning,  J48  Decision  Tree  Classiﬁers,  Natural  Language  Processing,	 	\nSearch	 Engines,	 Aggregated	 Search,	 Web	 Crawler.	 	\n \n \n2. Project  Name:Flexible  Software  Architecture for  Validation  of  Change	 	\nRequirements	 . ( Status:	 Completed	) 	\n \n● Development  of  Software  Fr	amework	 which  is  ﬂexible  enough  to	 	\nadjust	 in the	 scenario	 when	 the	 software	 requirements	 in an	 	\norganization	 continuously	 changes.	 	\n● Cost  of  implementing  change 	requirements  in  software  code  is 	reduced	 	\nby	 50%.	 	\n● Saving	 Man	-hours	 for	 writing	 code	 by	 50%.	 	\n \nTechno	logy/Tools:	 Java,	 JSP,Servlets,	 MySQL	 	\n \n3. Project	 Name:Question	-Answering	 Chatboat	 ( Status:	 Ongoing)	 	\n \n● Development  of  closed  domain	 Question	-Answering  Chatboat  on	 	\nEntertainment	 Domain.	 	\n● Development of  Open  domain Question	-Answering	 Chatboat  for Time	 	\nSeries	 Data	 using	 LSTM/BERT.	 	\n \nTechnology/Tools:	 Python,	 Machine	 Learning,	 Natural	 Language	 	\nProcessing.	 	\n \n \nITC	 InfoTech,	 Bangalore	 &mdash; Associate	 IT Consultant	 	\nOctober	 2008	 - March	 2009	 	\nProject	 Name:	 Monitoring	 Windchill	 Services,Client:	 PTC	 India	 Limited	 	\n \n● Successfully  h	andled  the  team  of  4  persons  for  conﬁguration  and	 	\nenhancement	 of &ldquo;Application	 Manager&rdquo;	 tool	 for	 reading	 different	 	\nservices	 Log	 Files	 and	 generating	 Statistical	 reports.	 This	 has	 increased	 	\navailability	 of different	 services	 of 	\nWindchill&rdquo;	 software	 by	 15%.	 	\n \nTechn	olo	gy/Tool	s: Java, JSP, Servlets, Or	acle 10g, Apache Tomcat 6.0. 	\n \nGeometric	 Limited	 &amp; 3dplmsoftware	 Solutions	 Ltd	 ( R &amp; D 	\nDivision of	 Dassault	 Systems), 	Pune	&mdash; Software	 Engineer 	 , 	\nAugust 2006 	-October	 2008	 	\n \nITC Infotech Ap	preciation	 	\nAward 	from ITC Infotech,	 	\nBangalore	 in 2014	 	\n \nInfosys Partner in Success	 	\nBronze	 Award	 from	 Infosys	 in 	\n2016	 	\n \nPATENTS	 	\n \nIndian Patent	 Published	 on	 	\n&ldquo;A Real World Object	 	\nInteraction Through Eyes: A	 	\nNovel Augmented Reality	 	\nConcept&rdquo;.	 	\n \nCERTIFICATIONS	 	\n \n● IBM	 Certiﬁed	 Database	 	\nAssociate	-DB2	 Universal	 	\nDatabase	 V8.1	 Family	 	\n● Brain	 bench	 Certiﬁed	 	\n(RDBMS,	 JSP,	 DB2	 	\nProgramming).	 	\n● Ematrix	 Application	 and	 	\nIntegration	 Certiﬁcation	 	\n(Certiﬁed by Geometric	 	\nLimited,	 Pune).	 	\n●  	MCP	 (Microsoft	 	\nCertiﬁed	 Professional).	 	\n● ANN	 and	 Deep	 Learning	 	\nCertiﬁed 	by MeitY and	 	\nIIITDMJ, Jabalpur\n\nProject Name: Enovia Matrix One Product Central, Client: LG Electronics	 	\n(South	 Korea)	 	\n●  	Successfully  delivered 	the  project  to  the	 client.  Worked  on 	\nComplete	 Software	 Lifecycle	 of the	 Project,	 Product	 Develo	pment,	 	\nFeature	 Enhancement,	 Bug	 Fixing	 , Unit	 Test	 Plan	 Creation	 and	 	\nDebugging.	 	\n● Modules 	 delivered 	 by 	 me 	 and 	 my	 team	 have  	 shown	 100%	 e    	 ciency	 	\non the client site	 when tested on	 large data	 sets.	 	\n \nTechnology/Tools:	 Java,	 Servlets,  JSP,  MQL,  TCL,  ema	trix,  oracle  9i,	 	\noracle 10g, Tomcat 5.5 	\n \nHarrier	 Information	 Systems,	 Nagpur	&mdash;System	 Engineer 	 , 	\nOctober	 2005	 -July	 2006	 	\n \nProject	 Name:	 Capital	 Market	 5 minutes	 Delayed	 Data,	 Client:	 NSE	 	\n(National	 Stock Exchange), CRISIL	 	\n \n● Successfully	 developed	 and	 delivere	d a Java	 System	 which	 	\nautomatically   	 downloads   	 all   	 the   	 market        information        ﬁles        on	 	\nreal	 time	 from	 NSE&rsquo;s	 production	 server 	 and 	 stores    on    our    local	 	\nsystem.	 	Further	 	the	 	Java	 	System	 	,automatically	 	dumps	 	the	 	\nﬁles(depending  upon  its  structure	 and      type)  into  respective  tabl	es  in	 	\nthe	 database.	 	\n● Development  of  this  project  results  in  100%  availability  of 	market	 	\ninformation	 ﬁles	 and	 its	 future	 data	 on	 our	 local	 system	 for	 future	 	\nanalysis	 of stocks.	 	 \nTechnology/Tools: 	Java, Servlets, JSP,  MultiThr	eading, JDBC,  Tomcat  5.5,	 	\nOracle	 9i 	\n \nSimsoft	 Technologies,	 Nagpur	&mdash;	System	 Administrator	 	, 	\nJuly	 2001	 -May	 2003	 	\n \nManaging	 and	 Handling	 LAN	 &amp; WAN,	 Troubleshooting	 the	 system	 and	 	\nthe	 network	 	\n \nTechnology/Tools:	 Java,JSP,Servlets,	 MYSQL,	 Oracle	 11g.	 	\n \n \nPUBLICATION	S 	\n \n● Authors	 : Abhijeet	 	Ramesh	 	Thakare,	 	Parag	 	S. 	Deshpande	, 	Title:	 	\nComparative	 Search	 of Entities	, Journal:	 International	 Journal	 of Software	 	\nEngineering	 	and	 	Knowledge	 	Engine	ering.	 	(SCI	 	Journal)	 	. 	\nhttp://ww	w.worldscientiﬁc.com/doi/abs/10.1142/S0218194017500498	 	\n● Authors  :  Abhijeet  Ramesh  Thakare,  Parag  S.  Deshpande, 	Title: 	Automatic	 	\nExtraction  of	 Attributes  and  Entities  for  Product  Differentiation	,  Journal:	 	\nInternational	 	Journal	 	of 	Computational	 	Intelligence	 	Systems	 	(SCI	 	\nJournal),	https://doi.org/10.2991/ijcis.11.1.23	 	\n● Authors  : 	Abhijeet  Th	akare,Nilesh  Korde 	Title:	Patient  Health  Monitoring	 	\nSystem	 Using	 Blue	 Eye	 Technology	 and	 IoT	, Journal:	 Bioscience	 	\nBiotechnology	 	Research	 	Communications	 	(WoS	 	(ESCI	 	Journal),	 	\nhttp://dx.doi.org/10.21786	/bbrc/13.14/3	 	\n● Authors 	:  Nilesh  Korde,  Abhijeet  Thakare	 Title	:Recognition  of  Letters  and	 	\nLetter	 Font	 in Images	 Using	 Deep	 Learning,	 Journal:	 Bioscience	 	\nBiotechnology	 	Research	 	Communications	 	(WoS	 	(ESCI	 	Journal),	 	\nhttp://dx.doi.org/10.21786/bbrc/13.14/20	 	\n● Authors 	:  Abhijeet  Thakare	 Title	:Intelligent  Automated  Irrigation  system	 	\nBased  on  Soil  Sensors  and  Weather  Forecast  Data. 	Journal: 	International	 	\nJournal	 of Next	 Generation	 Computing	 (WoS	 ( ESCI	 Jounal	).\n\nhttps://doi.org/10.47164/ijngc.v12i5.429	 	\n● Authors 	: Preeti	-Paranajpe  Voditel, Abhijeet  Ramesh  Thakare, 	Title	:  Use  of	 	\nSampling	 and	 Ant	 Colony	 optimization	 for	 Predicting	 support	 in 	\nRecommender  System, 	Conference	:  Science  and  Information  Conference  ,	 	\nUK,	 London,	 (Scopus)	, https://ieeexplore.ieee.org/document/6661794	 ( 	\nScopus	 Indexed)	 	\n● Authors : 	Nilesh Korde, Abhijeet Thakare	, Title: 	Smart Shelve Management	 	\nSystem  using  IOT	-A  Novel  approach	,  Con	ference: 	IEEE&rsquo;s  3rd  International	 	\nConference	 	in 	Technology	 	(I2CT),	 	Pune	 	(Scopus	 	Indexed),	 	\nhttps://ieeexplore.ieee.org/abstract/document/8529415	 	\n● Authors	 : Nilesh	 Korde,	 Abhijeet	 Thaka	re, Title:	Exploration	 of Opinion	 	\nfrom	 	Twitter	 	Data	, 	Journal:	 	International	 	Journal	 	of 	\nRecent	 	 Technology	 	and	  	Engineering	 	(Scopus	 	 	Indexed).	 	\nhttps://www	.ijrte.org/wp-co	ntent/uploads/papers/v8i4/B3854078219.pd 	\nf 	\n● Authors	 : Th a ka re   A. O.	, Thakare A. R., Tembhurne O.W.,Reddy S.N	., Title:	\nNoSQL Databases	-Modern Data Systems for Big Data An	alytics	-Features, 	\nCategorization and 	Comparison, 	 Journal:	 International Journal of 	\nElectrical and Computer Engineering Systems	(WoS	 (ESCI	 Journal) and 	\nScopus	 	 Indexed)	, DOI: 	https://doi.org/10.329	85/ij	eces.14.2.10	 	\n● Authors	 : Kha n da r   P,   T ha oke r   C,  	Thakare A	, Title: Decision Tree 	\nAlgoritm: 	A Survey, 13th International Conference on Advances in 	\nComputing, Control, and Telecommunication Technologies, ACT \n2022,	 2022, 8, pp. 985	&ndash;990	(Scopus	 Indexed)	 	\n● Authors	 : Th a ka re   A.	, Vodi t e l  P.	, Title: 	Extractive Text Summarization 	\nUsing LSTM Based Encoder	-Decoder Classification	, ECS Transactions	This 	\nlink is disabled	., 2022, 107(1), pp. 11665	&ndash;11672	 (Scopus	 Indexed)	 	\n 	\n \nEDUCATION	 	\nVisvesvaraya	 National	 Institute	 of	 Techn	ology	, Nagpur	 &mdash;	 	\nPhD	- Computer	 Science &amp; Engineering, 	January	 20 13	 - December	 20 17	 	\nDefense	 Seminar	 done	 in October	 2018.	 	\nCGPA:	 9.43	 	\nResearch	 Area	- Natural	 Language	 Processing,	 Web	 Mining,	 Text	 Mining,	 	\nInformation	 Retrieval	 	\n \nWalchand College of Engineering	, Sangl	i&mdash;	 Master of	 	\nEngineering	- Computer Science &amp; Engineering, 	August	 2003 	- August	 	\n2005	 	\nFinal	 Project	 Viva	 Done	 in October	 2006	 	\nPercentage:	 66.80%	 ( First	 Division)	 	\n \nP.C.E	 &amp; A, Nagpur	&mdash;	 Bachelor	 of Engineering	- Computer	 	\nTechnology,	 August	 1996	 - August	 200	1 	\nPer	centage:	 60.00%	 ( First	 Division)	 	\n \nA.G.	 Junior	 College,	 Dapoli	&mdash;	 HSSC	 (Maharashtra	 State	 Board)	 	\nJune 1995	 - March	 1996	 	\n \nPercentage:	 71.83%	 ( First	 Division)	 	\n \n \nA.G. Highschool, 	Dapoli	&mdash;	 SSC (Maharashtra State Board) 	June	 	\n1993	 - March	 1994	 	\n \nPercentage:	 78.85%	 ( First	 Division,	 Distinction)',2,2,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-08-08 12:42:02',0),(496,117,76,NULL,NULL,'76_117_75529296166b4bd1a5eade_final_aniket_cv_24.pdf','final_aniket_cv_24.pdf','aniket.sanyal@outlook.com +91-8421931940',2,1,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-08-08 12:42:02',0),(497,117,76,NULL,NULL,'76_117_49901470366b4bd1a5ed1f_Gokul_Talele_CV_(1).pdf','Gokul_Talele_CV (1).pdf','Gokul	 Talele	 	\nLead Data Scientist, Doctoral Researcher at SSBM, Geneva	 	 	\nPhone:             	7347278015 / 7798708015	 	 	Address:          	Pune, Maharashtra, India	 	 	LinkedIn:        	https://www.linkedin.com/in/gokul	-talele	 	 	Email:              	deva.talele@gmail.com	 	   \nA results	-driven	 Lead	 Data	 Scientist	 with	 a deep	 conceptual	 understanding	 of enterprise	-wide	 analytical	 needs	 and	 expertise	 in  Data	 Mining	 Methods,	 	\nPattern	 Classification,	 Quantitative	 Analysis,	 and	 Data	 Analysis.	 Possessing	 an inquisitive	 and	 analytical mind	 coupled	 with	 creative	 thinking,	 I excel	 in 	\nidentifying	 patterns	 and	 deriving	 actionable	 insights.	 With	 a customer	-focused  approach,	 I effectively	 manage	 the	 business	 cycle	 process	 from	 client	 	\nconsultation 	to 	closing	, ensuring	 strategic	 program  planning	 and	 realization	 of business	 benefits.	 Thriving	 in challenging,	 fast	-paced	 environments,	 I 	\nbring a	 detail	-oriented attitude,	 organizational	 skills,	 and	 a strong	 track	 record	 of delivering	 results.	 	\nEXPERIENCE	 	 \nAccenture,	 Pune	 	\nLead Data Scientist	 	\n \n \n     	June	 202	1 - Present	\nManaged  the  end	-to-end  lifecycle  of  the  AI  Tutor 	project,  from  conception  to  deployment,  ensuring  timely  delivery  and  alignment  with 	\nstakeholder requirements.	 	\nLed  a  team  in  creating  a  Quiz  AI  system  for  automatic  question  generation  and  answer  extraction,  integrating  it  seamlessly  in	to  the  AI  Tutor 	\nplatform.	 	\nConducted  data  analysis  to  derive  actionable  insights,  evaluated  model  performance,  and  iterated  on  algorithms  to  improve  eff	ectiveness 	\ncontinuously.	 	\nFostered cross	-functional collaboration and provided technical leadership and 	mentorship to junior data scientists.	 	\nBajaj	 Finserv,	 Pune	 	\nSenior Data Scientist	 	\nJuly	 2019	 - June	 2021	\nUtilized working knowledge of EDW (SQL), Pyspark, Python, R, Tableau, and Power BI to develop 	advanced analytical solutions. 	 	\nSpearheaded 	the 	development of machine learning models for financial fraud detection, leveraging techniques such as	 random forest, gradient 	\nboosting machines (GBM), and artificial neural networks (ANN) in Pyspark.	 	\nLed the creation of NLP models for data deduplication and customer base analysis, including 	implementing	 techniques like the Locality 	\nSensitive Hashing (LSH) model and similarity scoring in Pyspark.	 	\nDeveloped a propensity model using Pyspark for assessing the risk of customers, enhancing the organization&#039;s ability to mitig	ate potential 	\nrisks.	 	\nLeveraged Azure Databricks as the model development platform, ensuring scalability, reliability, and efficiency in the	 	\ndevelopment process.	 	\nApollo	 Munich	 Health	 Insurance	 Company	 Ltd.,	 Gurgaon	 	\nAssociate Manager 	- Analytics	 	\nSeptember	 2017	 - June	 2019	\nLeveraged  working  knowledge  of  SAS,  SQL,  R,  Python,  Tableau,  and  other  statistical  software  to  identify  opportunities  for  cre	ating  value	-	\nadded solutions to enhance business operations.	 	\nProactively identified and led initiatives 	to drive business growth and improve	 operational efficiency, demonstrating a strong drive to 	\nwin and a proactive working attitude.	 	\nStandardized and optimized the production of model validation reports, ensuring accuracy, consistency, and efficiency in the 	validation process.	 	\nConducted  hands	-on  reviews  and  validations  of  model  development,  including  examining  model  assumptions,  data  quality,  mathematical 	\nformulations, and independently recoding models for validation purposes.	 	\nDeveloped  risk  models  on  claims  data  to  assess  and  mitigate  potential  risks,  contributing  to  improved  decision	-making  and  risk  management 	\nstrategies.	 	\nDesigned comorbidity score models for claims handling, providing strategic business insights to management for 	informed	 decision	-making.	 	  \nPGIMER, Chandigarh	 	\nSenior Research Fellow	 	\nApril	 2016	 - August	 2017\n\nDeveloped questionnaire	-based tools for efficient data collection, facilitating comprehensive analysis and insights generation.	 	\nGenerated tables, listings, and graphs 	by protocols and assigned them to variables in analysis datasets using	 	\nR, SPSS, and other relevant tools.	 	\nDesigned and implemented advanced statistical and predictive models, leveraging cutting	-edge algorithms and diverse data sources to extract 	\nactionable insights.	 	\nUtilized analytical applications such as SAS, Python, R, and SPSS to identify trends and relationships within datasets, drawi	ng 	\nappropriate conclusions and translating findings into actionable recommendations.	 	\nStarcom	 Information	 Technology	 Limited,	 Bengaluru	 	\nStatistical 	Analyst	 	\nJuly	 2015	 - March	 2016	\nConducted detailed and comprehensive research on various statistical tools, staying abreast of advancements and best practice	s in the field.	 	\nProvided in	-depth analysis and 	recommendations on the suitability of different statistical tools for specific analytical tasks and projects.	 	\nDeveloped algorithms for implementation in software applications, ensuring efficient and effective utilization of statistical	 tools for data analysis 	\nand modeling.	 	\nCollaborated with cross	-functional teams to integrate algorithms into software platforms, optimizing performance and	 	\nusability.	 	\nIndian	 School	 of Business,	 Hyderabad	 	\nResearch Associate	 	\nNovember	 2014	 - June	 2015	\nLed data cleaning procedures and validation of analysis datasets and reports, ensuring data accuracy and reliability. Analyze	d customer trends to 	\ngain insights into underlying data processes.	 	\nUtilized SAS, R software, MS Excel, and databases like LexisNexis, ProQuest, and Factiva to work on graphs, frequency distrib	utions, 	the 	\nBass Diffusion model, segment analysis, and survival analysis.	 	\nGenerated tables, listings, and graphs 	per protocol and assigned them to variables in analysis datasets. Provided data support by extracting 	\ndata from various data warehouses using SAS.	 	\nConducted quality checks on entered data, cross	-validated generated reports, and generated data files from the Adapt data server. Created SAS 	\ndatasets from 	the data files.	 	\nDesigned and managed reporting processes, implementing standardized execution to enhance efficiency and accuracy. Leveraged t	echnology 	\nand reporting tools to identify and address potential issues, error	-proofing the system.	 	\nEmployed cutting	-edge methodologies for data mining and optimized algorithms to summarize and communicate key performance results 	\nand practice trends.	 	\nCompleted coursework for a Doctoral Level Programme in Causal Inference and Scientific Investigation at the Indian School	 	\nof Business.	 	\nEDUCATION	 	 \nSwiss	 School	 of Business	 and	 Management	 	\nDoctor of Business Administration, Business Administration and	 	\nManagement, General	 	\n \n \nApril	 2021	 - March	 2024	\nLiverpool	 John	 Moores	 University	 	\nMaster&#039;s degree, Data Science	 	\nGrade: 6.8/10	 	\n \nInternational	 Institute	 of Information	 Technology	 Bengaluru	 	\nPost Graduate Diploma, Data Science	 	\nGrade: 3.5/4.0	 	\n \nThe	 Maharaja	 Sayajirao	 University	 of Baroda	 	\nMaster&#039;s Degree, Biostatistics	 	\nGrade: 64%	 	\n \nNorth	 Maharashtra	 University	 	\nBachelor&#039;s Degree, statistics	 	\nGrade: 72%	 	\nJanuary	 2019	 - December	 2020	 	   \n \nJanuary	 2018	 - December	 2019	 	 \n  \n \nJanuary	 2012	 - December	 2014	 	  \n \n \nJanuary	 2009	 - December	 2012\n\nLANGUAGES	 	 \nEnglish	, Hindi	, Marathi	 	\nCERTIFICATIONS	 	 \nGenerative	 AI	 with	 Large	 Language	 Models	 	\nDeepLearning.AI, Amazon Web Services	 	\n \n \nArtificial	 Intelligence	 and	 Data	 Science	 for	 Leaders	 - Accenture	 	\nUniversity of 	Chicago Professional Education	 	\n \n \nNorthwestern	 University	 101:	 Leading	 with	 Analytics	 	\nKellogg Executive Education	 	\n 	\n \n \nFebruary	 2024	 - Present	 	 \n  \n 	\nOctober	 2022	 	\n   \n 	\nSeptember	 2022	\nPUBLICATIONS	 	 \nLung	 Cancer	 Detection	 and	 Recognition	 using	 Deep	 Learning	 	\nMechanisms	 for	 Healthcare	 in IoT	 Environment	 	\nInternational Journal of Intelligent Systems and Applications in	 	\nEngineering	 	\n \n \nCost	 optimization	 in CNN	 for	 big	 data	 of share	 price	 	\nAIP Conference 	Proceedings	 	\n 	\n \n 	\nJanuary	 2024	 	 \n   \n  \n \nNovember	 2023',2,0,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-08-08 12:42:02',0),(498,117,76,NULL,NULL,'76_117_196072348766b4bd1a5eea6_G-Prashanthi5.docx','G-Prashanthi5.docx','\r\nPrashanthi GannavarapuPrompt Engineer / Generative AI Engineer \r\nBengaluru, IndiaPhone: (+91)-8106907575Email: gprashanthi05@gmail.com/ prashanthi.gannavarapu22@iimb.ac.inProfessional Summary\r\nA seasoned Generative AI Engineer and Natural Language Processing (NLP) Expert, with a proven track record in leading teams to pioneer and implement cutting-edge AI solutions. Specializes in crafting compelling prompts using Open AI&rsquo;s GPT technology, enhancing user interactions with AI. Proficient in advanced machine learning techniques, adept at refining content and driving operational efficiency. Possesses a strategic mindset and employs a data-driven approach to consistently deliver transformative results, elevating stakeholder satisfaction.\r\nSKILLS \r\n&bull; Data Science Toolbox: Python, NumPy, pandas, seaborn, matplotlib, scipy, scikit- learn, Spacy, NLTK, Pytorch, Keras, TensorFlow, SQL, PySpark, GitHub, Docker, Tableau, Power BI, MS Azure Open AI \r\n&bull; Machine Learning Algorithm: Supervised: Linear Regression, Ridge/Lasso, Logistic Regression, Naive Bayes, Support Vector Machine, Decision Tree, Bagging - Random Forest, Boosting- XGBoost, AdaBoost.Unsupervised: K-Means, DBSCAN, Clustering. Topic Modelling : LSA, NMF, LDA. \r\n&bull; Deep Learning and Generative ai: ANN, CNN, RNN, LSTM, Transformer, Hugging Face, LLM. NLP - Embedding, Classification, Summarization, Sentiment Analysis ,Topic Modelling, Document Intelligence, Knowledge Mining, BERT, GPT-4, Llama, Langchain, Prompt Engineering , Generative AI  Microsoft Azure AI , Cognitive AI , Computational Linguistics, GPT-3/4, ChatGPT, Gemini, Microsoft Co-pilot, Decision Trees, LLM&rsquo;s , RAG Architecture , Computer Vision, DALL-E, MidJourney, Sora.Project Management: Jira , Git , ITSM Suite, Agile , ServiceNow , Miro, ServiceNow Reporting, Performance Analytics, Microsoft Excel, Lean Six Sigma.\r\nLicenses &amp;amp; certifications\r\nPrompt Engineering for ChatGPT , Vanderbilt University -  Issued Jul 2023\r\nGenerative AI with Microsoft Azure Open AI from Great Learning\r\nMicrosoft Azure AI Fundamentals (AI 900) Certification\r\nSix Sigma with Python - Green Belt Certified &ndash; Indian Statistical Institute(ISI) &ndash; Issued Nov 2023\r\nSAFe.5 Practitioner\r\nITIL 4\r\nServiceNow Performance Analytics &ndash; Micro Certification\r\n  EDUCATION:\r\nMicrosoft Azure Generative AI &ndash; Great Learning (Mar&rsquo;24 &ndash; June&rsquo;24)\r\nExecutive Programme in Business Analytics and Intelligence (BAI), IIM Bangalore (2022-2023)\r\nExecutive Program in Business Management (part-time) , IIM Lucknow (2016-2017)\r\nB. Tech, Electronics Communication Engineering- BPUT Rourkela, Odisha (CGPA- 7.4/10) \r\nWork Experience\r\nGenerative AI Engineer, Wadhwani AI(contract)      Aug 2023 &ndash; PresentProject Description:\r\nImplemented advanced generative AI techniques to develop an AI tutor tailored for students enrolled in entrepreneurship courses on the Moodle platform. Leveraged GPT-3/4, BERT, T5, and NLP models to create personalized learning experiences, offering guidance, resources, and feedback to enhance students&#039; understanding of entrepreneurship concepts and skills.\r\nTechnologies Used:\r\nGenerative Models: GPT-3/4, BERT, T5\r\nNatural Language Processing (NLP) Techniques: LLaMa, GEMINI, Named Entity Recognition (NER), Sentiment Analysis, Topic Modelling, Word Embeddings (Word2Vec, GloVe), Part-of-Speech Tagging\r\nLearning Management System: Moodle\r\nResponsibilities:\r\nDeveloped and fine-tuned AI-driven educational content, aligning with entrepreneurship curriculum objectives and student learning styles.\r\nTailored AI tutor prompts to individual student needs and preferences, optimizing engagement and comprehension.\r\nApplied generative AI techniques such as GPT models to dynamically generate personalized learning material, including explanations, examples, and quizzes.\r\nUtilized NLP techniques such as sentiment analysis and named entity recognition (NER) to ensure the content&#039;s conversational accuracy and relevance, categorizing responses into Red-Amber-Green (RAG) status for student progress tracking.\r\nCollaborated cross-functionally to integrate the AI tutor seamlessly into the Moodle platform, ensuring alignment with educational goals.\r\nConducted iterative refinement of content and prompts based on user feedback, leveraging NLP insights for continuous improvement.\r\nImplemented the Retrieval-Augmented Generation (RAG) architecture, integrating retrieval models with generative AI techniques such as GPT models and Large Language Models (LLMs) like T5 and Langchain for content generation.\r\nEnhanced content relevance and coherence by leveraging LLMs within the Retrieval-Augmented Generation (RAG) framework, facilitating the generation of contextualized responses, including explanations, examples, and quizzes.\r\nEnsured the AI tutor dynamically adapts to individual student needs using the Retrieval-Augmented Generation (RAG) approach, optimizing engagement and learning outcomes.\r\nCollaborated cross-functionally to integrate the RAG architecture seamlessly into the Moodle platform, ensuring alignment with educational goals and student requirements.\r\nConducted iterative refinement of content and prompts based on user feedback and RAG insights, maximizing learning outcomes and student satisfaction.\r\nEducational Break &amp;amp; Skill Enhancement (Jul &lsquo;22 &ndash; Jul &lsquo;23)\r\nUtilized a career break for educational pursuits and skill enhancement. Pursued Certification Programme in Business Analytics from IIM ,Bangalore.\r\nAcademic Projects:\r\nAccident Risk Score Prediction for Swiss Re: Led a project to develop a predictive machine learning model for determining accident risk scores at Swiss Re. Collaborated with actuarial teams and achieved improvement in prediction accuracy compared to existing methods.\r\nSenior Business Insights Analyst , Citrix Jan&rsquo; 21 &ndash; Jun&rsquo; 22\r\nTechnologies used: SQL, ServiceNow Performance Analytics, Naive Bayes, Logistic Regression, BERT ,  Tensorlfow, Keras , NLP &ndash; TF-IDF , NLTK, Pandas libraries\r\nProject: Revolutionized service desk operations through advanced analytics, predictive modelling, and NLP, significantly enhancing efficiency and customer satisfaction..\r\nBusiness Impact: Achieved measurable improvements in operational efficiency and customer satisfaction by identifying bottlenecks and predicting future support needs.\r\nResponsibilities: \r\nDeveloped and led a BERT-based Automated Ticketing System, significantly enhancing ticket categorization accuracy and user satisfaction.\r\nEmployed advanced NLP techniques for data pre-processing and model fine-tuning, achieving a 15% improvement in categorization accuracy over traditional models.\r\nManaged large datasets for model training, employing data cleaning, and augmentation to improve model performance and effectiveness.\r\nReduced ticket resolution times by 20%, directly impacting service efficiency and contributing to a 10% increase in customer satisfaction scores.\r\nCollaborated with cross-functional teams for seamless integration, optimizing the model for computational efficiency and establishing a continuous improvement feedback loop.\r\nSenior Consultant, CapgeminiJul &lsquo;20 &ndash; Dec &lsquo;20\r\nInnovated Ticket Categorization Process: Spearheaded a lean project to redesign the ticket categorization process, employing machine learning algorithms to automate and improve accuracy. This initiative resulted in a 30% reduction in ticket resolution time and significantly decreased manual categorization errors, enhancing overall service delivery efficiency and customer satisfaction.\r\nAgile Project Management: Managed cross-functional teams in a fast-paced, agile environment to deliver project milestones ahead of schedule, demonstrating exceptional leadership in driving project success and team synergy.\r\nStakeholder Collaboration: Worked closely with IT, quality assurance, and customer service teams to ensure the new ticketing process aligned with business needs and customer expectations, fostering an environment of continuous feedback and improvement.\r\nSenior Business Analyst, Compugain Solutions  Nov &lsquo;17 &ndash; Oct &lsquo;19\r\nLed a team of 10-15 associates using agile methodologies to optimize project delivery.\r\nDeveloped performance metrics, driving improvements in project delivery times and quality.\r\nExecuted talent development initiatives, enhancing team capabilities and project outcomes.\r\nAssistant Service Delivery Manager , Unisys Sep &lsquo;15 &ndash; Nov &lsquo;17\r\nManaged shift operations and team engagement using lean principles, focusing on continuous improvement.\r\nSpearheaded data analysis projects, enhancing decision-making processes and operational efficiency.\r\nOwned change management, leading the team through agile practice adoption to increase responsiveness.\r\nService Manager , Tech Mahindra Jan &lsquo;12 &ndash; Sep &lsquo;15\r\nIdentified and implemented improvements in team performance and service delivery using lean and agile frameworks.\r\nBuilt and executed initiatives for hiring and developing top talent, improving team capabilities.\r\nUtilized databases for data extraction and analysis, providing insights to inform business strategies.\r\n',2,0,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-08-08 12:42:02',0),(499,117,76,NULL,NULL,'76_117_12709312566b4bd1a5f07d_Gulshan_Kumar_MLEngineer.pdf','Gulshan_Kumar_MLEngineer.pdf','Guls h an  K um ar	\nMach in e l e a rn in g  e n g in eer	\n9103091562	 	g u ls h an ku m ar1 00g @ gm ail .c o m	 	lin ke d in .c o m /in /g u ls h an 100	 	Ja m mu, I n d ia	 	\ng it h ub.c o m /g u ls h an -1 00	 	\nE D U CAT IO N	\nBach elo r 	o f 	E n gin eerin g 	in  	C om pute r 	S cie n ce	\nU niv e rs it y  	o f 	J a m mu	\n12/2 020 &ndash;  0 7/2 024	 |  J a m mu,	 I n d ia	\nI N TER N SH IP S	\nM ach in e 	Le a rn in g 	In te rn	\nT a le n flo w\n06/2 024 &ndash;  p re se n t	\n&bull;D eve lo p ed  a  c h atb ot u sin g N LP  a n d  R AG	 	\nte ch n iq ues f o r r e sp on siv e  q uery  r e so lu tio n  o n  t h e	 	\nco m pan y&#039;s  w eb sit e .\n&bull;U til iz e d  O pen AI A P I, P yth on , a n d  P in eco n e D B.	\nD ata  	S cie n ce  	In te rn	\nZ id io  	D eve lo p m en t	\n02/2 024 &ndash;  0 3/2 024	\n&bull;C re a te d  a n d  e va lu ate d  p re d ic tiv e  a n aly tic s a n d	 	\npatt e rn  r e co gn it io n  m od els .\n&bull;Im ple m en te d  f e a tu re  e n gin eerin g, c la ssif ic a tio n ,	 	\nan d  r e g re ssio n  f o r p erfo rm an ce  o ptim iz a tio n .	\nP R O JE C TS	\nAI 	P ow ere d  	C alo rie  	B urn  	P re d ic tio n  	an d  	\nM an age m en t 	T o ol	\n05/2 024 &ndash;  0 6/2 024	\n&bull;D eve lo p ed  a  F la sk  w eb  a p p u sin g X G Boost t o	 	\np re d ic t e xe rc is e  c a lo rie  b urn  f r o m  a  K aggle	 	\nd ata se t.\n&bull;In te g ra te d  a  u se r-fr ie n d ly  i n te rfa ce  w it h  B oots tra p	 	\nfo r r e a l- t im e p re d ic tio n s.	\nP la n t 	D is e a se  	P re d ic tio n  	usin g 	C on vo lu tio n  	\nN eu ra l 	N etw ork s	\n04/2 024 &ndash;  0 5/2 024	\n&bull;D esig n ed  a  C on vo lu tio n al N eu ra l N etw ork  ( C N N)	 	\nm od el t o  c la ssif y  p la n t d is e a se s f r o m  l e a f i m age s.\n&bull;Im ple m en te d  a  w eb  a p plic a tio n  f o r i n sta n t d is e a se	 	\nd ia gn osis  u sin g u p lo ad ed  i m age s.	\nA I 	p ow ere d  	ch atb ot 	fo r 	T a le n flo w  	sta rtu p	\n06/2 024 &ndash;  p re se n t	\n&bull;B uil d in g a n  A I- d riv e n  c h atb ot w it h  R AG  a n d	 	\nO pen AI A P I t o  s tre a m lin e c u sto m er i n q uir ie s.\n&bull;E n han cin g u se r e xp erie n ce  t h ro u gh  i n tu it iv e ,	 	\na u to m ate d  r e sp on se s a n d  r e a l- t im e a ssis ta n ce .	\nS K IL LS	\nD ata  s c ie n ce , M ach in e l e a rn in g a n d  D eep  l e a rn in g\nPyth on , S u p erv is e d  a n d  u n su p erv is e d  a lg o rit h m s,	 	\nN eu ra l N etw ork s, A N N, C N N, R N N, L S T M , F e a tu re	 	\ne n gin eerin g, F e a tu re  s e le ctio n , N atu ra l L a n gu age	 	\nP ro ce ssin g e tc .\nP yth on  P ack age s a n d  F ra m ew ork s\nScik it -le a rn , T e n so rflo w , K era s, N um py, P an d as,	 	\nM atp lo tlib , N LT K , O pen C V\nLa n gu age s\nH TM L, C SS , J a va sc rip t, J q uery , N od e.js , F la sk\nD ata B ase s\nS Q L, P o stg re S Q L\nGen era tiv e  A I\nG PT, R AG , G em in i,  P in eco n e, H uggin g F a ce ,	 	\nLa n gch ain\nM LO PS\nG CP, G it , D ock e r, S tre a m lit	\nC ER TIF IC AT IO NS	\nMach in e l e a rn in g s p ecia liz a tio n\nD eep  l e a rn in g s p ecia liz a tio n\nLa n gch ain  f o r L LM  A p plic a tio n  d eve lo p m en t\nLa n gch ain  -  C hat w it h  y o u r d ata  ( d eep le a rn in g.a i)\nP ro m pt e n gin eerin g f o r d eve lo p ers\nS Q L f o r D ata  A n aly sis  -  I n te rn sh ala	\nO RG AN IZ AT IO NS	\nCheg g 	In d ia	\nS u b je ct 	M atte r 	E xp ert	\n0 5/2 023 &ndash;  p re se n t	\n&bull;Part t im e S u b je ct M att e r E xp ert a t C heg g,	 	\np ro fic ie n tly  a d dre sse d  o ve r 3 20 p ro bab il it y  a n d	 	\nsta tis tic s q uestio n s.	\nG eek sfo rG eek s\nTe ch n ic a l 	w rit e r	\n0 1/2 023 &ndash;  0 5/2 023	\n&bull;C on tr ib ute d  2 3 c le a r a n d  r e a d er-fr ie n d ly  t e ch n ic a l	 	\nartic le s t o  G FG , r e a ch in g o ve r 1 00,0 00 r e a d ers .',2,0,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-08-08 12:42:02',0),(500,117,76,NULL,NULL,'76_117_142130915366b4bd1a5f225_HAMZA_SHAKEEL_IBM.pdf','HAMZA_SHAKEEL_IBM.pdf','HAM ZA  S H AK EEL	\nPYT H O N D EVE LO PER	\nham za .9 595@ gm ail .c o m	 	8808916347	 	Lu ck n ow	 	lin ke d in .c o m /in /h am za -s h ake el- 5 21b 36135	 	\nP ro fil e\nE xp erie n ce d  P yth on  D eve lo p er w it h  e xp ertis e  in  d esig n in g a n d  m ain ta in in g P yth on  s c rip ts  fo r s y ste m  t a sk s a n d  d ata\np ro ce ssin g.  P ro ve n  a b il it y  t o  im ple m en t b est p ra ctic e s f o r c o d e r e a d ab il it y ,  m ain ta in ab il it y ,  a n d  p erfo rm an ce .  S kil le d\nin  d eve lo p in g a n d  d ep lo yin g m ach in e le a rn in g m od els ,  s p ecia liz in g in  n atu ra l  la n gu age  p ro ce ssin g a n d  p re d ic tiv e\na n aly tic s.\nE d uca tio n\nBach elo r 	o f 	T e ch n olo gy, 	S R M CEM	05/2 014 &ndash;  0 8/2 018	 |  	L u ck n ow ,	 , I n d ia	\nP ro fe ssio n al E x p erie n ce\nD ata  	A I 	D ev e lo p er, 	K yn d ry l	0 2/2 022 &ndash;  p re se n t	 |  	G urg a o n ,	 I n d ia	\n&bull;P ro fic ie n t i n  i m ple m en tin g a n d  f in e-t u n in g m on it o rin g s o lu tio n s u sin g P ro m eth eu s a n d  G ra fa n a.\n&bull;D ep lo ye d  a n d  C on fig u re d  P ro m eth eu s m on it o rin g f o r B harti A ir te l E n han cin g 5 g  r e a d in ess o f t h e c lie n t\n&bull;Exte n siv e  i n vo lv e m en t i n  L in ux/U nix  s y ste m  a d m in is tra tio n , t ro u b le sh ootin g a n d  n etw ork in g i n  L in ux e n vir o n m en t\n&bull;VM s/p ro je ct d ep lo ym en t a n d  h an d ove r t o  t h e c u sto m er, o th er t a sk  i n clu d e V M  r e b uil d , m an agin g c o m pute s,	 	\nc o n tro lle r a s w ell a s a p plic a tio n  h ea lt h  a n d  s e rv ic e s.\n&bull;C on tr ib ute d  i n  d eve lo p m en t o f a n  A I- d riv e n  c h atb ot l e ve ra gin g P yth on  a n d  H uggin g F a ce &#039;s  L LM  m od els , i n clu d in g	 	\nG oogle P alm 2 t o  p ro fic ie n tly  m an age  c u sto m er i n q uir ie s c o n ce rn in g A ir te l&#039;s  5 G  s e rv ic e s. I n te g ra te d  L a n gC hain  f o r	 	\nad va n ce d  l a n gu age  p ro ce ssin g, f o ste rin g c o n tin ual e n han ce m en ts  i n  c o m pre h en sio n  a n d  r e sp on se  q ualit y	\nS yste m  	En gin eer(P yth on  	d ev e lo p er), 	T a ta  	C on su lt a ncy  	S erv ic e s	1 0/2 018 &ndash;  1 1/2 021	 |  	B an ga lo re ,	 I n d ia	\n&bull;I m ple m en te d  a d va n ce d  a lg o rit h m s i n  P IX E R  t o  o ptim iz e  c o p y i t e m  s e le ctio n  b y i n te g ra tin g f e a tu re s l ik e  P C  c h ars ,	 	\nW eig h t C hars , a n d  d eta il e d  r e ta il e r i m age s, s ig n if ic a n tly  e n han cin g o p era tio n al e ffic ie n cy  a n d  a cc u ra cy  t o  d riv e	 	\no ve ra ll b usin ess s u cce ss.\n&bull;In it ia te d  w it h  d ata  c le a n in g, h an d lin g m is sin g v a lu es a n d  o u tlie r d ete ctio n  t o  e n su re  h ig h  d ata  q ualit y  a n d  r e lia b il it y .	  	\n&bull;A p plie d  a d va n ce d  N LP  p re p ro ce ssin g t e ch n iq ues i n clu d in g l o w erc a sin g, a lp han um eric  f il t e rin g, s te m min g,	 	\nto ke n iz a tio n , a n d  s to p  w ord s r e m ova l t o  p re p are  t e xtu al d ata  i n  P IX E R  f o r s u b se q uen t m od elin g s ta ge s.\n&bull;P erfo rm  f e a tu re  e n gin eerin g t o  d eriv e  n ew  f e a tu re s t h at m ig h t e n han ce  m od el p erfo rm an ce  ( n um ber_ ch ara cte r,	 	\nn um ber_ se n te n ce , n um ber_ w ord s).\n&bull;U til iz e d  B ag o f W ord s a n d  T F-ID F f o r t e xt d ata  v e cto riz a tio n , w it h  h yp erp ara m ete r t u n in g s p ecif ic a lly  t o  T F-ID F t o	 	\no ptim iz e  p ara m ete rs  s u ch  a s m ax_ fe a tu re s a n d  n gra m _ra n ge\n&bull;E n han ce d  m od el p erfo rm an ce  t h ro u gh  a d va n ce d  t e ch n iq ues l ik e  S M OTE ( S yn th etic  M in orit y  O ve r-s a m plin g	 	\nTe ch n iq ue) a n d  A D ASYN  t o  a d dre ss d ata  i m bala n ce  i s su es, c ru cia l f o r a cc u ra te  p re d ic tio n s i n  s k e w ed  d ata se ts .\n&bull;U til iz e d  m ach in e l e a rn in g a lg o rit h m s i n clu d in g R an d om  F o re st, L in ea r S VC  ( S u p port V ecto r C la ssif ie r), L o gis tic	 	\nR eg re ssio n , a n d  M ult in om ia l N aiv e  B aye s ( M NB),  f o r m od elin g a n d  p re d ic tio n  t a sk s.\n&bull;Im ple m en te d  h yp erp ara m ete r t u n in g u sin g g rid  s e a rc h  w it h  c ro ss -v a lid atio n  t o  s y ste m atic a ll y  o ptim iz e  m od el	 	\np ara m ete rs , e n su rin g r o b ustn ess a n d  g e n era liz a b il it y .\n&bull;U til iz e d  e va lu atio n  m etr ic s s u ch  a s p re cis io n -r e ca ll c u rv e s a n d  F 1  s c o re s t o  a ss e ss m od el p erfo rm an ce	 	\nc o m pre h en siv e ly .	\nS kil ls\nP yth on , M ach in e L e a rn in g, P an d as, M yS Q L, L LM , L a n gC hain , G en  A I, P ro m eth eu s, G ra fa n a, L in ux, E xc e l, D ock e r, A W S',2,0,1,'Recruiter',NULL,'N',14,0,NULL,NULL,0,NULL,NULL,'2024-08-08 12:42:02',0),