The platform instinct
Enterprise AI is a systems problem. The model matters enormously, but the production value comes from the product and infrastructure around it: data contracts, execution controls, audit trails, rollbacks, and operational clarity.
My DataRobot work spans core modeling, trust and explainability, and current agentic AI platform capabilities. That gives me a practical bridge between predictive AI maturity and the new execution surfaces created by generative AI.
Proof points
| Claim | Evidence | Artifact |
|---|---|---|
| Enterprise AI has to connect predictive and generative systems. | DataRobot work spans AutoML, Trust and Explainability, Global MCP, skills, tools, sandboxing, and long-running agents. | DataRobot AI Platform |
| Trust features need architecture and operating discipline. | Root Insights, Lean Testing, storage reduction, release readiness, and incident command work shaped regulated customer-facing platform surfaces. | Resume |
| Enterprise AI needs infrastructure that is boring in the right places. | Feature flags, entitlements, smoke tests, rollback, monitoring, and evals bound the risk of fast-moving AI capabilities. | Enterprise AI infrastructure |