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Naveen Kancharla
Mid-level AI & Backend Engineer specializing in RAG systems and scalable APIs
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Built and deployed a production LLM-powered document Q&A system using a strict RAG pipeline (LangChain-style orchestration + FAISS) to help users query large internal document sets. Demonstrates strong reliability focus through hallucination mitigation, curated offline evaluation with grounding checks, and production monitoring (latency/fallback rates) plus stakeholder alignment via demos and business metrics.
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Key Strengths
- Built and deployed a production LLM document QA system for internal knowledge search
- Practical hallucination reduction using strict RAG, similarity thresholds, and abstention behavior
- Structured evaluation approach combining offline test sets, manual retrieval validation, and grounding checks
- Production-minded monitoring and iteration using latency, fallback rates, and user feedback
- Able to debug complex multi-step LLM workflows via step-level logging and prompt versioning
- Thoughtful model/retrieval/prompt selection based on accuracy, latency, cost, sensitivity, and scale constraints
- Effective communication with non-technical stakeholders via demos and outcome-based success metrics
Reference Highlights
- Strong at designing and shipping LLM-based products
- Led an LLM-based QA document system end-to-end (architecture through deployment)
- Active and consistent communication (daily updates, progress communication)
- Resolves technical challenges effectively
- Plays a key role in shipping production solutions
- Works very well across teams and with non-technical stakeholders
- Aligns on requirements early
- Translates technical details into business-related terms
- Uses simple examples to explain technical concepts
- Ensures understanding through open discussion
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