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Yashwant Gandham
Junior Machine Learning & Backend Engineer specializing in LLM systems and ML infrastructure
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About
Built and deployed production RAG-based document search/Q&A systems (DocChat and an internship marketing RAG), using a React + FastAPI stack on GCP with docs stored in GCP buckets and retrieval via embeddings/vector DB. Emphasizes cost/performance tradeoffs (reported ~40% cost reduction) and ships via Docker (Railway), with load/API testing using JMeter and Swagger; regularly collaborates with a CEO stakeholder to iterate and push changes to production.
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Key Strengths
- Built and deployed a full-stack RAG document search/Q&A system on GCP
- Cost/performance optimization (reduced LLM cost ~40% by retrieving only relevant chunks)
- Practical retrieval design using structured chunking (headings/subheadings) and chunk metadata
- Production-minded deployment experience (Dockerized RAG system deployed on Railway)
- Reliability/testing focus using JMeter for load testing and Swagger UI for API endpoint testing
- Strong collaboration loop with non-technical stakeholder (CEO) to iterate, test, and ship to production
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