I build AI systems that actually make it to production, and write about what I learn along the way.

As a kid, I was obsessed with space. Documentaries, books, the whole thing. So I did the very on-brand nerd move and studied physics, then astrophysics, at the University of Lisbon.

Somewhere along the way I realized the part I loved most wasn't the stars themselves, it was taking messy data and pulling a real conclusion out of it. That pulled me toward data science, which honestly felt like physics with a different dataset.

Then I got curious about what happens after the model works in a notebook. How do you deploy it? How does the whole end-to-end thing actually hold together in production? That question turned me into an AI engineer, and I haven't looked back.

Since then I've built and shipped AI in very different settings: computer vision and NLP research in Berlin, anomaly detection in Amsterdam, large-scale forecasting and ML pipelines at one of Europe's biggest energy companies, and today, production AI services, owning everything from data ingestion to deployment and monitoring.

Alongside the engineering, I write. I've published 15+ articles on Towards Data Science and Medium, and I run Learn AI, a newsletter read by 3,500+ people every month. My angle is simple: I'm genuinely excited about AI, I build with it every day, but the most useful thing I can do is stay grounded. I care about what AI can do and about the tradeoffs, the costs, and how to use it well anyway.

If that sounds like your kind of approach, I'd love to hear what you're working on.