Fernando Mosqueda

About

Healthcare AI engineer with hands-on ownership of production ML and LLM systems at McKesson, spanning clinical risk prediction and RAG-based documentation tools. Stands out for combining deep clinical-data experience, HIPAA-aware deployment practices, and measurable impact through reduced readmissions, clinician workflow gains, and 20% to 30% faster ML delivery for engineering teams.

Key Strengths

• end-to-end ownership of production clinical ML systems
• healthcare AI domain expertise
• deployment of low-latency scalable ML services on AWS/FastAPI/Docker/Kubernetes
• strong production monitoring and drift mitigation
• LLM/RAG implementation for clinical documentation
• quality and safety evaluation with clinician review and fairness checks
• HIPAA-conscious AI system design
• pragmatic tradeoff management across latency, cost, accuracy, and safety
• iterative improvement of rare-case handling in production
• developer productivity improvements through reusable Python libraries
• reduced prototype-to-deployment time by 20% to 30%
• business impact through reduced readmissions and estimated $10K-$15K savings per avoided readmission

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