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Ritvik Nimmagadda
Junior AI/ML Software Engineer specializing in LLMs and MLOps
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About
Built and productionized an AI-native, agentic appeals decisioning system for health insurance operations, automating 500k+ scanned appeals/year. Delivered measurable impact by cutting review time from 12–15 minutes to ~3 minutes and auto-resolving ~85% of cases with strong auditability, evaluations, and human-in-the-loop guardrails, deployed as containerized microservices on Azure AKS.
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Key Strengths
- Owned and shipped end-to-end AI decisioning workflow (frontend, backend, data, infra) on Azure AKS
- Scaled production automation for 500k+ health insurance appeals/year
- Delivered major ops efficiency gains (review time reduced from 12–15 minutes to ~3 minutes)
- Achieved high automation rate with human-in-the-loop guardrails (~85% auto-resolved)
- Strong LLM/RAG evaluation and safety practices (regression datasets, hallucination tracking, schema validation, confidence thresholds)
- Production reliability and migration expertise (strangler pattern, contract tests, shadow traffic, canaries, rollback plans)
- Operational excellence (runbooks, on-call procedures, monitoring/tracing, replay strategy)
- Effective stakeholder partnership translating ambiguous exec goals into an iterative roadmap
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Experience
Education
Awards
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Publications
2 publications
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