AI Engineering Leadership
Building systems that make AI useful, reliable and understandable in the real world.
What I do
I work at the intersection of engineering leadership, software delivery and AI — helping teams move from experimentation to systems that create measurable value.
AI across the engineering lifecycle
Requirements → Development → Testing → Delivery → Support → Improvement.
The interesting work is not simply putting an AI model into a product. It is designing the surrounding systems, workflows, controls and feedback loops that make the technology useful in practice.
How I work
Discover → Experiment → Reflect → Share.
I prefer practical experimentation to theoretical prediction. I build things, try tools, measure what happens, and share the useful parts — including the things that don’t work.
Featured project
Personal Finance AI Advisor
Bringing together UK and NL finances with AI-powered insights, context and explainable suggestions.
Recent writing
- The AI Engineering Stack: What Actually Belongs Around the Model?
- From AI Demo to Production: What Changes When the System Has Consequences?
- AI Agents at Work: How Much Autonomy Should You Give an AI?
- Constrained AI in Regulated Industries: A 4–6 Week Experiment
- How Do You Know Your AI Actually Works? Build an Evaluation Harness
Understand the problem
Build with AI
Evaluate behaviour
Deploy safely
Observe reality
Learn and iterate