Senior Director · Cloud & AI Engineering
Beyond the demo. Into production.
I help organizations build cloud and AI platforms that continue delivering value through strong architecture, engineering, and operating models.
Known For
Where the work has concentrated.
Four areas of practice, in the order they've shaped how I think.
- Enterprise Architecture
- Long-lived platforms for financial services, banking, and enterprise SaaS. The kind that get audited every year and still work every day.
- Cloud & Platform Engineering
- Azure and .NET at enterprise scale. Cloud as a compounding capability, not a migration project that ends the moment the servers move.
- Applied AI
- Moving AI from a working demo to something the business can run without holding its breath. Currently building AI-powered platforms that help organizations interact with complex data more naturally, without giving up governance or trust.
- Engineering Leadership
- Architecture and teams that outlast the roadmap that created them. Building engineering capability, mentoring, and staying hands-on where the difficult decisions actually get made.
Principles
Five ideas most of my work returns to.
Short enough to fit on a napkin. Load-bearing enough that most decisions come back to one of them.
- 01
Technology succeeds when ownership is clear.
Velocity is easy to measure. Long-term ownership isn't. The difference decides which systems survive year three.
- 02
AI readiness matters more than AI adoption.
Programs fail because organizations are unprepared, not because models are weak. The prerequisites (data access, evaluation, governance, workflow fit) do most of the work.
- 03
Architecture is as much about operating models as software.
Boundaries, standards, and accountability are architectural choices. Elegant systems still fail when they don't fit the organization that has to run them.
- 04
Cloud is an operating capability, not a migration project.
The technology decisions rarely decide the outcome. Platform investment, standards, and developer experience are what compound over time.
- 05
Engineering quality is measured years after deployment.
The signal is whether the system remains understandable, operable, and safe to change once the people who built it have moved on.
Contact
Advisory, architecture, and speaking conversations.
For advisory engagements, architecture reviews, or leadership conversations in enterprise architecture, cloud, or applied AI, reach out with a short note on the audience or the problem.