Before cloud and AI, I spent years in the manufacturing and test engineering of IBM mainframes — the most quality-critical hardware in the industry. That foundation is why my software holds up in production.
Most software engineers have never seen what happens when quality fails at scale. I spent the early part of my career making sure IBM mainframes — machines that banks, airlines, and hospitals depend on for zero-downtime operation — left the factory correct. Every unit. Every time.
In German industrial engineering, this kind of background is understood. Precision, quality discipline, and manufacturing-scale thinking are not soft skills — they are engineering prerequisites. I bring them directly into how I design software systems today.
Test and Product Engineer across IBM mainframe manufacturing operations in Brazil and the United States.
Systematic test case design, coverage analysis, failure-mode enumeration, and regression discipline — applied at hardware level before it became a software practice.
ISO 9000 quality processes, defect tracking, root-cause analysis, and corrective action in a manufacturing environment where rework is expensive and field failures are unacceptable.
Understanding how components interact within a system — and how failures propagate. This is the mental model I apply to distributed software systems today.
Working across engineering, manufacturing, compliance, and customer teams to resolve issues under time pressure — the same muscle used in software incident response.
Manufacturing lines have hard deadlines, hard quality gates, and no tolerance for "close enough". That precision carries directly into how I treat production deployments.
Supporting R&D projects for major enterprise customers (Cisco, NCR, Hitachi) instilled the habit of owning outcomes, not just deliverables.
The habits I built on the IBM manufacturing floor are the same habits that make my software reliable in production. I write tests before I ship. I model failure modes before I architect. I treat production incidents with the same seriousness I once treated a field return on a $3M mainframe. The tools changed — the discipline didn't.
This background is why I approach software architecture with more than just code in mind. I think about what happens when the system fails, who is affected, and what the recovery path looks like. That instinct was trained in an environment where failure was not an option — and it transfers directly to building production software that organisations depend on.
In the German Mittelstand — companies like WEISS GmbH, where I work today — this combination of manufacturing rigour and modern software capability is not just valued; it is exactly what the market needs as industrial companies invest in their own digital and AI transformation.