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Sanjana Duvva
Mid-level AI/ML Engineer specializing in Generative AI, LLMOps, and MLOps
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About
Built and deployed an AWS-based LLM/RAG ticket triage and knowledge retrieval system (Pinecone/FAISS + Step Functions + MLflow) that cut support resolution time by 20%. Demonstrates strong production focus on hallucination reduction, PII security, and low-latency orchestration, with measurable evaluation improvements (e.g., ~25% grounding accuracy gain via re-ranking) and proven collaboration with support operations stakeholders.
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Key Strengths
- Built and deployed production LLM-powered ticket triage + knowledge retrieval system on AWS
- Reduced support ticket resolution time by 20%
- Hallucination mitigation via strict RAG constraints, cross-encoder re-ranking, answer validation, and prompt guardrails
- Designed reliable multi-step RAG orchestration with Step Functions (parallel branches, custom retries, rollback paths)
- Evaluation-driven iteration: improved grounding accuracy ~25% by changing retrieval strategy and adding re-ranking
- Strong approach to agent/workflow reliability: modular design, unit + scenario testing, A/B testing, metric-driven monitoring (accuracy, grounding, latency, human override rate)
- Effective collaboration with non-technical stakeholders to translate operational pain points into requirements (labels, thresholds, fallback behaviors)
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