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Sirisha Maddikunta
Mid-level Generative AI Engineer specializing in enterprise LLM and healthcare AI solutions
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About
Built and owned an end-to-end LLM-powered fraud investigation assistant that automated case summaries and risk analysis, cutting analyst investigation/documentation time by 40%. Stands out for translating RAG concepts into a production-grade internal platform with strong evaluation, monitoring, and reusable Python service architecture that improved both analyst trust and engineering velocity.
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Key Strengths
- Owned end-to-end production LLM system from prototype to deployment and monitoring
- Delivered 40% reduction in fraud analyst investigation and documentation time
- Strong RAG system design for dynamic, real-time data use cases
- Improved retrieval quality through metadata filtering and re-ranking
- Balances latency, cost, and quality tradeoffs using production feedback
- Built scalable internal APIs integrated into analyst workflows
- Established shared Python service layer to reduce duplicated ML/LLM logic
- Improved team velocity and onboarding through standardized APIs and reusable components
- Grounded evaluation approach using labeled test cases, retrieval checks, and analyst feedback
- Focus on reliability via testing, schemas, containerization, and CI
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