Beyond daily operations, I ran global infrastructure programs — coordinating IP migrations across 16+ international locations, leading Change Management governance, building company-wide infrastructure metrics reporting for senior leadership. These were my first lessons in cross-functional delivery: you can know every technical detail and still fail if you can’t align teams, manage stakeholders, and communicate clearly across time zones.
Moving into consulting — first at Infosys, then FICO, and later at Hitabis GmbH in Berlin — I was exposed to a completely different model: you walk into a customer environment, quickly understand their constraints, and deliver. At Hitabis I operated as the primary infrastructure engineer, introducing containerization with Docker and early Kubernetes, building automation pipelines, and delivering monitoring infrastructure for major German enterprise customers. This is where I learned that infrastructure agility is not just about tooling — it’s about how teams think about change.
Joining Docker in 2018 was a turning point. Docker was not just a company — it was at the center of a fundamental shift in how software was built and run. Supporting Docker Enterprise customers meant engaging with some of the most complex container adoption challenges across global enterprises. I worked across Docker Enterprise, Swarm, Kubernetes, UCP, and DTR — and watched organizations transform how they thought about application delivery, platform reliability, and operational scale.
When Mirantis acquired Docker’s enterprise business, I moved into a Solutions Architect role — a shift from reactive support to proactive architecture. Today I partner with enterprise customers to design and deliver Kubernetes platforms using MKE, k0s, and MSR. I have led platform upgrades across complex multi-environment estates, migrated multi-terabyte container image repositories, and modernized Kubernetes ingress architectures supporting thousands of backend applications. Each engagement is different. The common thread is helping organizations move from fragile, undocumented infrastructure to production-grade platforms they can actually operate.
The next chapter in platform engineering is already starting to take shape. AI workloads are putting new demands on infrastructure — GPU scheduling, high-throughput networking, cost visibility, inference optimization. The teams building AI platforms are discovering the same lessons enterprise Kubernetes teams learned five years ago. My focus is on understanding this space deeply: not just as a technical challenge, but as an architectural and economic one. That’s where platform engineering is heading — and it’s where I want to be.