From AI engineer to infrastructure engineer — and back.

Twenty years ago, I stood at the intersection of curiosity and innovation, doing PhD research on neural networks at University POLITEHNICA of Bucharest — captivated by one question: how does data shape the way machines learn and behave?

While my peers focused on theoretical models, I was poking at patterns we'd now call data poisoning and prompt engineering. I didn't know it then, but I was asking questions that wouldn't go mainstream for two decades.

The pivot that changed everything

In 2005, Microsoft knocked. I faced a choice: keep pushing toward a PhD in a field the world wasn't ready for, or apply AI/ML thinking to real production systems. I took my Master's and dove into platform engineering. What looked like a departure from AI was actually the start of a deeper journey — learning how distributed systems behave at scale, how to design resilient infrastructure, how to lead teams through genuinely hard problems.

Building the foundation

At Microsoft I started in Windows Phone platform engineering — designing test frameworks and leading the Windows Insider program — and learned what it takes to ship software that millions of people depend on. I discovered that great engineering isn't just writing code: it's understanding systems, anticipating failures, and building for scale.

Then I moved into cloud infrastructure: CI/CD automation that cut deployment times by 85% and bugs by 83%, validation frameworks for Azure services like Event Grid, Key Vault, and Cosmos DB, and systems that recovered from regional outages in thirty minutes instead of days.

Each project taught me something new about resilience, automation, and the balance between innovation and reliability.

Full circle: AI returns

Then came 2020, and AI exploded into the mainstream. Suddenly the questions from my PhD research were everywhere — and I had something rare to bring: fifteen years of production engineering to apply to them.

I built a production RAG system that tripled accuracy to 76%, processing 20–30 incidents daily. I architected an Artifact Cache Monitor that reduced incidents by 90% and bandwidth waste by 99.8%. I'm now developing predictive worker-scaling systems that use ML to optimize resource allocation by 25–30%. The researcher who left academia in 2005 came back — not with theories, but with production-grade AI systems running at Microsoft scale.

What drives me today

I don't just build AI systems; I bridge worlds — connecting cutting-edge research with the messy reality of production. I take ideas out of papers and turn them into things that survive real traffic, real failures, real constraints. My edge isn't just understanding neural networks or Kubernetes or distributed systems — it's understanding how they fit together, how to make them work in production, and how to lead teams through the complexity.

I hold a patent for objective application-rating systems and have another pending for AI-driven predictive scaling. But the patents aren't the point. The point is solving problems that matter and mentoring the people who'll take this further than I will.

The thread

The questions I asked in 2005 are still the questions I ask today. The difference is that now I have the tools to answer them — and a place to write the answers down honestly.