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Diwita Banerjee
Mid-level AI/ML Engineer specializing in LLMs, RAG, and enterprise AI
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About
Built an enterprise RAG-based document intelligence system at Freddie Mac for regulatory and financial documents, helping analysts cut search time from hours to minutes while improving retrieval accuracy by ~30%. Stands out for combining LLM product delivery with compliance-grade auditability, production monitoring, and scalable Python/FastAPI service design.
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Key Strengths
- End-to-end ownership of production RAG/LLM systems
- Enterprise document intelligence for regulatory and financial use cases
- Improved analyst search time from hours to minutes
- Boosted retrieval accuracy by around 30%
- Strong focus on auditability with source citations and metadata grounding
- Balanced latency, cost, and quality in production ML systems
- Reduced hallucination risk through strict grounding and refusal logic
- Iterative production optimization using monitoring, drift detection, and user feedback
- Modular Python/FastAPI service design for team scalability
- Built reliable deployment workflows with Docker and Jenkins CI/CD
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1 publication
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