Tech Lead |Software Architecture & Production Systems

The EU AI Act entered into force in August 2024. Fourteen months later, most enterprise engineering teams still don't have a working understanding of what it actually requires from them. That gap is becoming expensive.

Human-in-the-loop means something different when the loop includes children. The power asymmetry, the regulatory environment, and the pedagogical requirements of K12 education raise the bar for what meaningful human oversight actually requires — and most educational AI systems aren't meeting it.

Teams treat retrieval-augmented generation as something you add to an AI system. It isn't. It's a foundational architectural choice that determines what the system can and can't do reliably. Getting this distinction wrong is expensive.

Most AI learning roadmaps are built for people starting from zero. This one is built for someone who already knows how production systems fail, and wants to apply that instinct to enterprise AI without wasting 18 months on the wrong things.

I transitioned from Analytical Chemistry to AI after a PhD studying PAH detection. Here's how scientific thinking and the use of AI in Chemistry shaped how I learn Artificial Intelligence.

I came into RAG with strong production architecture instincts and assumed they'd transfer cleanly. Some did. The places where they didn't are where I made my biggest mistakes — and where most teams I've talked to are making theirs.

Discover why a seasoned tech lead decided to dive into the world of Artificial Intelligence. In this post, I share the mindset shift, learning roadmap, and career goals behind my journey from mobile development to AI — and how you can start yours too.

Enterprise software is built on determinism. AI systems are fundamentally probabilistic. That mismatch is the root cause of most enterprise AI reliability failures — and closing it requires a different mental model, not just better engineering.