Infrastructure architect → AI engineering leader.

I lead the AI Engineering & Governance function at Siemens Digital Industries Software — a team I built from the ground up, hiring and developing AI engineers who ship agentic systems, RAG platforms, and AI-enabled engineering tooling. I use AI coding agents daily and review their output with the same rigour I'd apply to any engineer's code. That balance — staying hands-on while leading — is the whole job.

I got here through infrastructure, not application development. An aerospace engineering degree, then a decade architecting cloud platforms — high-availability Azure estates, deployment automation, compliance architecture — across Siemens and Microsoft. Platform engineering taught me the instincts that AI enablement now runs on: systems that fail safely, pipelines you can trust, and governance that engineers don't route around.

Operating principles

• Narration is not review. Whether the code came from an engineer or an agent, read the diff. Confidence in the description is not evidence about the output.
• Governance should be light enough to hold. Controls that create friction get routed around, and then you have neither control nor visibility. Enough to sleep at night, no more.
• Judgment transfers; specifics don't. Write the pattern, not the incident. It's why this site exists and also how it stays out of trouble.

What this site is

Personal writing on enterprise AI adoption, infrastructure, and engineering leadership. Everything work-derived stays at the pattern level — lessons and approaches, never internal specifics, names, or numbers. Off the clock, a homelab (Tailscale mesh, self-hosted everything) keeps the infrastructure judgment honest.

The best way to reach me is email . The longer version of the career is on the CV page.