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Harshitha Mittapalli
Mid-Level Full-Stack Software Engineer specializing in cloud-native and GenAI solutions
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About
Built and shipped production RAG-based LLM agents automating multi-step document query workflows, emphasizing reliability via monitoring, retries, structured exception handling, and fallback retrieval (alternative embeddings/keyword search). Demonstrated measurable gains (18% latency improvement, 25% retrieval efficiency, 12% precision) and has experience integrating agents with messy tax and transaction data at RSM using validation/cleaning and idempotent design.
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Key Strengths
- Shipped production RAG agent with monitoring, fallbacks, and structured exception handling
- Improved response latency by 18%
- Improved semantic retrieval efficiency by 25%
- Improved retrieval precision by 12% via embedding iteration and error analysis
- Designed predictable multi-step agent workflows with typed schemas, orchestration/state management, and input validation
- Reliability engineering for LLM systems (retries, alerts, graceful degradation on timeouts/low-confidence retrieval)
- Experience integrating agents with messy tax/transaction data including validation, cleaning, and idempotency safeguards
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