Fourteen years making systems reliable — now applied to production AI.

Production AI needs the same reliability rigor that safety-critical engineering brings. That conviction is the throughline of my work — and it is why I describe what I do as a rare combination rather than a career change.

For fourteen years I have built complex systems to be safe and dependable in regulated environments. Today that means automotive systems engineering at Audi and CARIAD — the Volkswagen Group’s software company — including functional safety under ISO 26262 and ASPICE, where a defect is a recall, not a bug ticket. I trained as a Dipl.-Ing. in mechatronics at TU Dresden, and added deep-learning and MLOps training — DeepLearning.AI on Coursera, AI/ML nanodegrees at Udacity — to point that engineering discipline at applied AI. The full record is in my CV and on LinkedIn.

I have also been the operator, not only the engineer. Several of the systems I have built ran inside an operating business, where I was the person responsible for whether a rollout survived contact with real users. That view is why I treat adoption as a behavioural problem as much as a technical one — the hardest part of a system is usually getting people to rely on it.

I own systems from the data model to deployment, so the architecture stays coherent end to end. I am deliberate about cost — much of practical AI engineering is knowing where not to call the model — and in everything I build, AI is a powerful component, never the whole product.