Interim IT leadership for regulated companies - when stability, compliance or delivery cannot fail.

AI earns its place in a regulated business only when the systems around it are reliable, secure, observable and auditable. That intersection is where I work: a senior infrastructure and platform architect who also designs and builds modern AI systems hands-on - so the technology reaches production without becoming the next audit finding.

I treat AI as an engineering and governance problem, not a demo. I assess where models genuinely add value, design the data flows, guardrails and evaluation around them, and give leadership a clear-eyed read on risk, cost and control - the same discipline I bring to any business-critical platform.

• LLM & RAG systems, built for productionRetrieval-augmented and LLM-backed systems with grounding, evaluation and fallbacks - engineered, not prompt-and-pray.
• AI governance & model riskControls, policies and documentation that hold up to DORA / BaFin-style ICT-risk and auditability expectations.
• Data protection by designTraining, retrieval and inference paths kept minimised, compliant and traceable end to end.
• Vendor-independent by defaultModels chosen and integrated without lock-in, with a routing layer that keeps cost and control in-house.
• Observability & evaluationMonitoring for quality, drift and cost so an AI feature stays trustworthy long after launch.

I've followed this field since a dedicated Artificial Intelligence course in my computer-science studies (2015/16) - well before the hype - and have watched it mature from the infrastructure, security and governance side, not the vendor stage.