Must have skill / Mandatory	Mandatory – MLOps, Google Cloud Platform GCP and Vertex AI.


Key Responsibilities
•	Responsible for building and maintaining robust machine learning pipelines ensuring efficient model deployment monitoring and lifecycle management within a cloud-based environment
•	Extensive expertise in MLOps specifically with Google Cloud Platform GCP and Vertex AI and a deep understanding of model performance drift detection and GPU accelerators
•	Build and maintain scalable MLOps pipelines in GCP Vertex AI for endtoend machine learning workflows
•	Manage the full MLOps lifecycle from data preprocessing model training and deployment to model monitoring and drift detection
•	Implement realtime model monitoring and drift detection to ensure optimal model performance over time
•	Optimize model training and inference processes using GPU accelerators and CUDA
•	Collaborate with cross functional teams to automate and streamline machine learning model deployment and monitoring
•	Utilize Python 310 with libraries such as pandas NumPy and TensorFlow to handle data processing and model development
•	Set up infrastructure for continuous training testing and deployment of machine learning models
•	Ensure scalability security and high availability in all machine learning operations by implementing best practices in MLOps
 
Requirements
•	5 years of experience in MLOps and building ML pipelines 3 years of experience in GCP Vertex AI
•	Deep understanding of the MLOps lifecycle and automation of ML workflows
•	Proficient in Python 310 and related libraries such as pandas NumPy and TensorFlow
•	Strong experience in GPU accelerators and CUDA for model training and optimization
•	Proven experience in model monitoring drift detection and maintaining model accuracy over time
•	Strong problemsolving skills with the ability to work in a fast paced environment
•	Knowledge of data versioning and model version control techniques
•	Familiarity with TensorFlow Extended TFX or other ML workflow orchestration frameworks