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Suresh Kunchala
Mid-level Full-Stack Developer specializing in FinTech and enterprise web platforms
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About
Financial-services AI engineer who shipped a production investment research assistant using RAG over internal research reports, SEC filings, and meeting transcripts, with a strong emphasis on truthfulness and guardrails. Built a structured evaluation loop (200+ golden test cases, RAG Triad metrics) that directly improved retrieval quality (e.g., fixing year-mismatch retrieval, boosting sensitive-query performance by 18% and cutting hallucinations to near zero) and scaled ingestion to ~10k messy documents with RabbitMQ + OpenTelemetry.
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Key Strengths
- Shipped real-time, performance-sensitive UI features; optimized state management to reduce unnecessary re-renders
- Built production RAG investment research assistant over internal research, SEC filings, and meeting transcripts
- Designed high-truthfulness guardrails for LLMs in high-stakes financial decisioning (rule-based + human review)
- Implemented structured LLM eval loop with 200+ golden Q/C/A triplets including edge cases and negative tests
- Used RAG Triad metrics (context precision, faithfulness, relevance) to drive retrieval improvements
- Diagnosed retrieval errors (2022 vs 2023 filings) and improved sensitive-query results by 18% while reducing YoY hallucinations to near zero
- Built scalable async ingestion/processing pipeline; handled spikes to ~10k documents using RabbitMQ
- Strong observability practices using OpenTelemetry to pinpoint bottlenecks (OCR latency, embedding API rate limits)
- Pragmatic cost/latency tradeoffs: SLMs for entity extraction; reserved larger models for complex reasoning/synthesis
- End-to-end delivery of real-time financial risk dashboards (frontend + backend)
- Performance optimization via lean REST payloads and query/index tuning
- Production-grade Python API design (JWT auth, URL versioning, layered architecture)
- Reliability engineering with unit/integration testing (pytest) and observability (Sentry, Prometheus, Grafana)
- Scalability troubleshooting using PostgreSQL execution plans and AWS CloudWatch
- AWS containerized deployments on ECS with GitHub Actions CI/CD and zero-downtime releases
- Designing PostgreSQL schemas for massive historical trading logs with stored procedures for audit/reporting
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