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Venu Dave
Junior Software Development Engineer specializing in backend data platforms and LLM applications
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About
Amazon internship experience building and shipping an end-to-end NL-to-SQL system: ingested/normalized metadata across 60+ internal tables, added rigorous multi-layer validation for LLM-generated SQL, and served it via a FastAPI backend for engineers—driving 90%+ faster dataset discovery and ~70% lower effort to access data. Also built an early-stage RAG-based healthcare assistant, iterating on chunking, embeddings, and retrieval to improve answer quality post-launch.
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Key Strengths
- Owned end-to-end production data/ML pipeline (ingestion → validation → transformation → serving)
- Built NL-to-SQL pipeline over 60+ internal tables with schema-context ingestion
- Multi-layer validation for LLM-generated SQL (schema checks, syntax validation, controlled execution, fallback regeneration)
- Delivered measurable impact: 90%+ reduction in dataset discovery time and ~70% less effort to access data
- Designed resilient ingestion with idempotency, retries, and safe reprocessing/backfills
- Shipped internal FastAPI service exposing NL-to-SQL with performance and reliability optimizations
- Iteratively built and improved an early-stage RAG healthcare assistant (chunking/embeddings/retrieval tuning)
Reference Highlights
- Strong end-to-end ownership of data pipelines
- Excellent reliability and data-quality outcomes
- Significantly reduced query failures
- Improved data accessibility
- Strong Python and SQL in real production work
- Ensures correctness via validation and edge-case handling
- Effective debugging to maintain reliable outputs
- Improved system performance by reducing unnecessary joins/context
- Built stable pipelines that reduced manual intervention
- Strong logging and monitoring practices
- Designed reliable idempotent pipelines enabling safe retries/backfills
- Prevents downstream issues through validation and schema handling
- Highly effective generalist in ambiguous environments
- Scopes problems well and makes practical tradeoffs
- Ships end-to-end with minimal direction
- Consistently follows through to improve systems
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