Sanjay N
GenAI, RAG and Agentic AI Engineer
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Assessing RAG System Quality Using RAGAS
Built an automated RAG evaluation pipeline using RAGAS
Evaluated RAG outputs on critical metrics:
Faithfulness
Answer Relevance
Context Precision
Context Recall
Multimodal Faithfulness & Relevance Created a structured evaluation dataset with questions, ground truth answers, retrieved context, and model responses Used evaluation results to identify retrieval vs generation failures Visualized metric scores to support model and prompt improvements
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Multi-Agent Incident Management AI Assistant
An intelligent multi-agent system that leverages Langgraph Multi agent system and LLM to provide contextual incident management support. The system intelligently routes queries to specialized agents:
Incident Data Management Agent - Uses MCP Server to connect to SQL Server and retrieve real-time incident data
Incident Management SOP Agent - Uses MCP Server to connect to Elasticsearch and performs semantic search to retrieve incident management procedures
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AI-Powered Conversational Data Analytics Platform
Built an end-to-end AI-driven data analytics platform that allows users to upload datasets (CSV/Excel), ask natural-language questions, generate insights, visualize data, and create interactive dashboards — all without writing a single line of code.
The system uses agent-based GenAI architecture to automatically interpret user queries, select the right analytical approach, compute results, and render appropriate visualizations or dashboards in real time.
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