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Durgaprasad G
Mid-level AI/ML Engineer specializing in LLM infrastructure, RAG, and agentic systems
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About
Stripe engineer who owned and unified multiple team RAG systems into a shared production platform used by 200+ internal operators, deployed on EKS with Kafka ingestion and hybrid retrieval. Drove measurable business outcomes including <400ms latency, ~35% inference cost reduction, ~25% accuracy lift via fine-tuning, and real-time auto-approval of 80%+ merchant compliance applications through strong observability and reliability patterns.
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Key Strengths
- Owned shared RAG platform end-to-end (ingestion to deployment) used by 200+ internal operators
- Performance optimization: kept latency under 400ms via caching and reranking tradeoffs
- Cost optimization: reduced inference costs ~35% through batching and reuse of results
- Production monitoring/observability with OpenTelemetry dashboards and SLO compliance tracking
- Delivered major automation impact: merchant compliance agent auto-approved 80%+ of applications in real time
- Applied research-to-production: implemented hybrid retrieval and improved recall by 32% after document-splitting changes
- Built reliable internal Python services with gRPC, rate limiting, retries, and circuit breakers to prevent cascading failures
- Improved model quality: fine-tuned LLMs on Stripe data for ~25% accuracy gain with structured-output validation and iterative fixes
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