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Deepanjay Nandal
Software Engineering Intern specializing in real-time analytics and distributed systems
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About
Built a production AI legal search platform that uses a retrieval-first, source-grounded LLM pipeline with confidence-based fallbacks and structured, traceable outputs to reduce hallucinations and improve trust. Also has experience at Discover Excellence building real-time analytics and identity stitching systems, emphasizing conservative data validation, idempotent processing, and fault-tolerant queue-based workflows.
Experience
Education
Key Strengths
- Shipped production LLM legal search with retrieval-first grounding to minimize hallucinations
- Designs deterministic multi-step agent pipelines with explicit tool/function boundaries and structured state passing
- Strong safe-failure patterns (confidence gating, fallback responses instead of guessing)
- Reliability-focused iteration via monitoring feedback loops (tuned chunking/ranking/thresholds to reduce low-quality answers)
- Experience hardening systems against messy real-world data (validation, conservative identity stitching, idempotency, queue-based isolation)
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