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Haneesh Kapa
Junior AI Full-Stack Engineer specializing in LLM automations and RAG systems
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About
Built and shipped a production LLM-powered customer support assistant using a Python/FastAPI backend with RAG (embeddings + vector search) over internal docs and product/operational data. Instrumented the system with logging/metrics and ran continuous eval loops; post-launch improvements focused on retrieval quality (chunking/ranking) and performance/cost tradeoffs (query classification, caching, validation guardrails).
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Key Strengths
- Shipped production AI customer support automation end-to-end (architecture → build → launch → iterate)
- Designed scalable RAG workflow integrating structured + unstructured internal data sources
- Balanced accuracy, latency, and API cost via query classification and caching/structured data paths
- Built observability into systems early (logging, analytics, latency and success/fallback metrics)
- Implemented LLM guardrails (context-constrained prompting, response validation, safe fallback behavior)
- Runs structured LLM evaluation loops using real user queries, human review, and automated regression tests
- Improved answer quality by diagnosing retrieval as root cause and refining chunking and ranking
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