Who guides you
I was afraid of becoming obsolete — so I went from Cloud Architect to AI Engineer. Not by outsourcing everything to a chatbot, but by learning the concepts companies actually pay for. I show you exactly what matters and what's in demand.
Today I have a well-paid AI engineer job — but only because I built and understood new things on the side of my day job. I want to help you do the same.
Engineers get paid to solve problems. How are you going to do that if you don't understand the concepts?
The path
Before AI was the default answer to every problem, I worked as a Cloud Architect — AWS and Azure certified, production systems, the boring reliability work that keeps products alive. That foundation still shapes how I judge AI tooling: if you can't explain the system, you don't own it.
Language models started shipping more code than the developers around me — faster, often cleaner. Standing still stopped feeling safe. The scary version of AI isn't that it replaces you overnight. It's that you keep prompting while someone else learns to explain why the answer is wrong.
I didn't quit and hope. After hours I built the things I needed to understand: RAG chatbots, voice agents, research agents, full-stack AI apps. Enough that I could walk through failure modes, evals, and architecture — not just a polished demo.
The well-paid AI engineer role came after that work, not before it. Interviews and production both reward the same thing: can you solve the problem when the demo breaks? That only works if you own the concepts.
Blog, free courses, Masterclass, membership — same bet: use AI as a multiplier, not a replacement for understanding. Less vibe-coding theater. More mental models you can defend in a room full of hard questions.
Where to go next