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Ishaan Nanal
Intern-level Software Engineer specializing in backend systems and AI/ML
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About
Built and shipped an LLM-powered RAG research copilot used by 20+ users across biology, physics, and ML, cutting literature review from days to minutes. Strong focus on production reliability—iterated on chunking/retrieval/prompting, added validation and modular pipelines for debuggability, and is now containerizing and scaling the system with Docker and GCP.
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Key Strengths
- Shipped end-to-end RAG research copilot to production
- Reduced technical literature review time from days to minutes
- Iteratively improved retrieval quality via chunking/retrieval/prompt refinements
- Designed predictable multi-step agent workflow with explicit state passing for debuggability
- Built reliability into pipelines with validation checks and safe-failure handling for messy data
- Experience working with inconsistent biological datasets and enforcing quality thresholds
- Built an AI research copilot end-to-end from scratch (Next.js + TypeScript)
- Designed product UX around slow/opaque retrieval flows to improve usability
- Pragmatic engineering approach: simple first, iterate for reliability/performance
- Postgres data modeling for analytics workflows (records/processed/runs) with PK/FK constraints
- Improved query performance via indexing and validated with execution timing and query plans
- Modular RAG pipeline design enabling independent retrieval improvements without breaking generation
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