1. Roles & Responsibilities: 2/10  
2. Years of Experience: 2/10  
3. Education Qualification: 6/10  
4. Skills/Knowledge: 2/10  
5. Domain/Industry: 2/10  
6. Key Competencies: 2/10  
7. Certifications: 0/10  

<h5 style="font-family: Roboto; font-size: 0.88rem;"><b>Overall Summary</b></h5>  
<ol style="font-family: Roboto; font-size: 0.88rem;">
  <li><b>Roles & Responsibilities:</b> The candidate’s experience is primarily in middleware integration using IBM IIB/ACE and MQ. There is no mention of developing or deploying machine learning models, performing data cleaning, feature engineering, or using Python-based ML libraries. The resume lacks any reference to predictive modeling, classification, clustering, or model evaluation metrics like precision, recall, or AUC.</li>
  <li><b>Years of Experience:</b> The resume shows 8+ years of experience, which falls short of the required 12 to 15 years. This is a significant gap given the strict requirement for senior-level experience.</li>
  <li><b>Education Qualification:</b> The candidate holds a B.Tech in Applied Electronics and Instrumentation Engineering from Asansol Engineering College. While the degree is technical, it is not directly aligned with AI or data science, but still acceptable for scoring purposes.</li>
  <li><b>Skills/Knowledge:</b> The resume lacks core skills required for the role such as Python, Pandas, NumPy, Scikit-Learn, SQL, regression models, classification models, and cloud platforms like Azure. The candidate’s skill set is focused on IBM middleware technologies, ESQL, and integration tools, which are not relevant to the ML/AI-focused job requirements.</li>
  <li><b>Domain/Industry:</b> The candidate has worked in banking, automotive, and manufacturing domains, but there is no indication of experience in AI, ML, or LLMs. The resume does not reflect any exposure to artificial intelligence or related technologies.</li>
  <li><b>Key Competencies:</b> While the candidate demonstrates collaboration, stakeholder communication, and project coordination, there is no evidence of problem-solving in AI/ML contexts, deriving insights from data, or staying updated with AI/LLM trends. The competencies are more aligned with integration and middleware support roles.</li>
  <li><b>Certifications:</b> There is no mention of any certifications related to Python, ML, AI frameworks, cloud platforms, or data science tools. This is a critical gap for a role requiring strong technical expertise in AI/ML.</li>
</ol>