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Akash Patil
Mid-Level Software Engineer specializing in backend systems and LLM/RAG applications
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About
Backend/AI engineer at Intuit who built a production AI-powered case assistant for support agents (FastAPI on AWS EKS) combining Postgres case data, OpenSearch retrieval with embedding reranking, and internal LLMs. Improved peak-season reliability by diagnosing P95/P99 timeout spikes and cutting P95 latency from ~800ms to <400ms via composite indexing, keyset pagination, connection pool tuning, and caching, while adding grounded-generation guardrails (evidence packs, confidence thresholds, fallbacks, human-in-the-loop).
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Key Strengths
- Built and shipped an AI-powered case assistant API for customer support agents (FastAPI on AWS EKS) integrating Postgres, OpenSearch retrieval/reranking, and internal LLMs
- Debugged intermittent P95/P99 latency spikes under burst traffic using distributed tracing and DB/query analysis
- Database performance optimization under concurrency (composite indexes, keyset pagination, reduced overfetching, connection pool tuning, caching)
- Designed safe, grounded LLM outputs with citations/evidence packs, confidence indicators, and fallbacks to prevent hallucinations
- Designed multi-step agent-like workflows with structured outputs, confidence-based verification, and controlled retries/escalation logic
- Production Python maintainability practices: layered architecture, strong typing/Pydantic contracts, async IO discipline, pinned dependencies, CI checks, and on-call observability
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