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Subhash Mothukuru
Mid-level Machine Learning Engineer specializing in LLMs, generative AI, and MLOps
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About
Built and shipped a production LLM-powered medical scribe that generates structured clinical visit summaries using RAG, strict JSON schemas, and post-generation validation to reduce hallucinations. Experienced in making LLM workflows deterministic and observable (structured logging/metrics/tracing) and in evaluation-driven iteration with metrics like schema pass rate and edit rate; collaborated closely with clinicians and policy stakeholders at Scale AI to drive adoption.
Experience
Education
Key Strengths
- Built and deployed an LLM-powered medical scribe system to production
- Strong reliability focus: strict schemas, validation, guardrails, deterministic fallbacks, retries
- Practical hallucination reduction via schema enforcement and post-generation validation
- Cost/latency optimization through prompt tightening, context pruning, token caps/early stopping, caching
- End-to-end observability design (structured logs, metrics, tracing with request IDs)
- Evaluation-driven development using golden datasets, regression tests, and production quality metrics
- Effective cross-functional collaboration translating clinician/policy needs into measurable constraints and metrics
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