Mid-level AI/ML Engineer specializing in Generative AI, LLMOps, and MLOps
AI/ML Engineer5 years experienceMid-LevelFinancial ServicesBankingHealthcare
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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.
Experience
AI/ML EngineerWells Fargo
AI/ML EngineerUnitedHealth Group
Software EngineerTarget Corporation
Education
University of North Texasmaster, Computer Science
Andhra Universitybachelor, Computer Science
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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