About me
I am a Senior Cloud Solution Architect at Microsoft and a hands-on builder. My work sits where AI strategy has to become a real system: agentic applications, evaluation, observability, enterprise integration, and the engineering it takes to move from prototype to production. Before that I spent years across data platforms and machine learning consulting, which is where Relataly started. Outside customer work I build products and open-source projects, from source-linked AI research and ML-driven market tools to Swiss German speech benchmarks and an embodied-agent robotics simulator. Building things myself keeps my architecture advice honest.
- Role
- Senior Cloud Solution Architect at Microsoft
- Based in
- Zurich area, Switzerland
- Education
- PhD, University of Bremen (2018)
What I work on
- Agentic systems and orchestration
- AI evaluation and observability
- Azure and Microsoft Foundry architecture
- Speech and multimodal AI
- Data platforms and ML engineering
- Workshops, writing, and technical storytelling
Background
- Current
Microsoft: applied AI and cloud architecture
Working with customers and teams on Azure, Microsoft Foundry, agentic applications, evaluation, and the path from AI prototype to production.
- Ongoing
Independent products and open source
Building and running public products and open-source projects: AIUseCaseHub, Rankoon, PerpXray, speech benchmarks, coding-agent evaluation, and robotics simulation.
- Since consulting
Public AI and ML publishing
Writing practical articles and notebooks about machine learning, data science, analytics, and generative AI on Relataly, based on topics I explored as a consultant and builder.
- Until 2018
PhD, University of Bremen
A research foundation that still shapes how I think about systems, adoption, innovation, and the gap between ideas and implementation.
Research
My research at the University of Bremen explored service innovation, modularity, and collaboration in automotive engineering.
Speaking
- What it takes to move from AI demos to systems people can use
- Agentic systems, orchestration, and where the hard parts start
- Evaluating agents and models with evidence instead of vibes
- MCP and tool ecosystems: making enterprise knowledge usable for agents
- AI governance, sovereignty, and architecture trade-offs
- How data platforms change when AI becomes part of everyday work