EXPERIENCE / Senior Applied AI / ML Engineer
Seven-plus years across scientific computing, healthcare prediction, analytics products, private document intelligence, and production agentic AI.
Career progression
Scientific computing, healthcare products, and accountable AI.
Today Claims and document agents with explicit validation, evaluation, and human authority.
Role chronology
From biomedical research to healthcare products and production AI.
Production claims and document agents with typed workflow state, validation, exception routing, evaluation, observability, and explicit human authority.
Predictive healthcare ML, local OCR/RAG, HEDIS evidence extraction, transportation FWA analytics, interoperability, MLOps, and operational reporting.
Built a 0-to-1 healthcare analytics platform across nine programs, connecting risk models, data delivery, embedded analytics, and care-manager workflows.
Hospital operations and provider-data analytics, validation, reporting, and workflow automation.
Explainable readmission modeling, healthcare ETL and automation, and the early product foundation that later scaled across programs.
Clinical signals, genomics, scientific computing, statistical modeling, and research communication established the evidence discipline behind later systems.
Methods by era
Each stage added a new layer without erasing the statistical and data foundations beneath it.
Clinical signals, genomics, research computing, and statistical reasoning established the habit of keeping lineage and assumptions visible.
Readmission, utilization, hospital operations, and provider-data work connected model quality to the workflows that consume it.
Risk models, data pipelines, embedded reporting, and care-manager workflows became one reusable product across nine healthcare programs.
Local OCR/RAG, healthcare quality evidence, predictive models, transportation anomaly review, and MLOps operated inside a payer data boundary.
Document and claims workflows combine typed state, tools, deterministic controls, exception routes, evaluation, and trace telemetry.
Education
M.S., Biomedical Informatics — University of Chicago, 2019
Summer School, Public Health Modeling — Yale University, 2019
B.Tech., Computer Science & Engineering — SRM University, 2018
Credentials
AWS Certified Machine Learning Engineer — Associate
Microsoft Azure AI Fundamentals (AI-900)
Microsoft Power BI Data Analyst (PL-300)
Leadership pattern
Translate between operations, domain experts, data, models, platforms, and reviewers.
Make uncertainty, ownership, and release evidence explicit before scale.