Platform and AI engineering that holds up in production.
I help engineering organizations clarify difficult technical decisions, build shared platforms, and turn useful AI ideas into dependable systems. My work combines staff-level judgment with hands-on implementation.
Design shared platforms and paved roads that reduce developer friction without hiding operational reality.
Platform architecture, AWS and Kubernetes foundations, observability, infrastructure as code, reliability, and developer experience.
Production AI systems
Move AI ideas beyond demos into tools that are useful, observable, secure, and maintainable.
MCP servers, agentic applications, internal AI tooling, integrations, evaluation strategy, guardrails, and operational readiness.
Technical strategy
Turn ambiguous engineering problems into a practical direction teams can execute with confidence.
Architecture reviews, build-versus-buy decisions, platform roadmaps, adoption plans, technical leadership, and team enablement.
When to bring me in
A useful fit for consequential engineering work.
Your platform has become a collection of infrastructure tickets instead of a product for developers.
An internal AI prototype is promising, but the path to secure and reliable production use is unclear.
Teams are losing time to fragmented tooling, weak observability, or inconsistent delivery practices.
A consequential architecture decision needs an experienced, independent technical perspective.
Experience
Built from production work, not a playbook.
I've spent more than a decade building software across startups and larger engineering organizations. My recent work spans shared AWS platforms, Kubernetes, Terraform, observability, automated compliance, production MCP servers, agentic applications, and organization-wide AI adoption.
That range helps me connect strategy to implementation: the architecture has to make sense, but it also has to work for the engineers who will operate and extend it.