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Jai Vilatkar
Junior AI/ML Developer specializing in GenAI, LLM agents, and RAG systems
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About
Built and shipped an agentic RAG chatbot module for NexaCLM to answer questions across large volumes of contracts while minimizing hallucinations and incorrect legal interpretations. Implemented routing between vector retrieval and ReAct-style agent retrieval plus an automated grading/validation layer (cosine-similarity thresholds, retries) and deployed via GitHub Actions to Azure Container Apps, partnering closely with legal stakeholders to define risk/clause-focused objectives.
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Key Strengths
- Built and deployed a production agentic RAG chatbot for contract/legal Q&A with hallucination reduction focus
- Designed query routing between vector retrieval and agent-based retrieval (ReAct) to improve reliability and latency
- Implemented answer/document grading with cosine-similarity thresholds and retry/rephrase loops for safer outputs
- Created structured test suites for routing correctness (answer present/absent/partial) and output quality evaluation
- Strong LLMOps mindset: deterministic workflows, guardrails, measurable metrics, and production logging/analysis
- End-to-end delivery including CI/CD via GitHub Actions and deployment to Azure Container Apps
- Effective collaboration with non-technical legal stakeholders; translated legal needs into AI objectives and feedback loops
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