Architecture decisions on record, not just outcomes.
I build AI programs inside regulated enterprises—insurance, financial services, healthcare—where the business context is as demanding as the engineering. The technical choices that got to production are what I publish.
My delivery base is insurance and regulated-industry programs—environments where a wrong architecture call carries regulatory and financial cost. That friction taught me to pair every technical decision with a business constraint and put both on record.
The org changes before the cloud does.
Across cloud migrations, AI platforms, and enterprise data programs, the consistent failure point is governance and team structure—not the stack. I address the org design first; the technology lands faster because of it.
The result: programs that close on time, with costs tracked, and with teams that own the outcome after I leave. No advisory theater—delivery with a paper trail.
Milestones measured, not titled.
Enterprise Integration Delivery
Agile Transformation — 8 Teams
Led API integration programs for three insurance carriers. Cut policy issuance cycle time by 30% through event-driven architecture replacing batch processing.
Restructured delivery across eight cross-functional teams, moving from waterfall to scaled agile. Deployment frequency increased 4x within 18 months.
Cloud Modernization — 40+ Services
AI Programs in Production
Shipped claims prediction, fraud detection, and customer analytics platforms. Each program transferred model ownership to the business team by week 12.
Migrated legacy on-premise workloads to AWS and Azure. Reduced infrastructure operating cost by 35% and eliminated two data center contracts.
Enterprise program governance
Models that reach production
Org-first migration design
Portfolio-level governance, risk mitigation, and stakeholder alignment across multi-year transformation programs in regulated industries.
End-to-end AI program delivery—from data architecture through model deployment—with measurable cost reduction and risk outcomes on record.
Cloud migrations scoped around operating model change first, technology second—preventing the common failure of moving an old org chart to a new cloud.