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Deepika Dhanajayan
Mid-level Data Scientist specializing in Generative AI, RAG systems, and ML engineering
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About
AI/LLM engineer who built a production QA RAG for a University of Massachusetts faculty success initiative, cutting service tickets by 70%. Strong end-to-end RAG implementation skills (LangChain, Qdrant, hybrid/HyDE retrieval, FastAPI) with rigorous evaluation (RAGAS, LLM-as-judge) and practical handling of constraints like API rate limits and cost. Prior cross-functional delivery experience collaborating with SMEs and business owners at TCS and IBM.
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Key Strengths
- Built and deployed a production QA RAG that reduced service tickets by 70%
- Pragmatic architecture tradeoffs balancing performance and cost (paid LLM vs self-hosted fallback; vector store selection)
- Hands-on RAG evaluation using RAGAS (faithfulness, answer relevancy) and LLM-as-judge
- Designed reliable LLM workflows with modular document processing, chunking, hybrid retrieval, and grounding via function calling to structured databases
- Implemented production backend patterns (FastAPI, Pydantic validation) with observability/tracing (LangSmith)
- Cross-functional communication with business stakeholders via demos and impact framing (time savings, ticket reduction)
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