THE STORY
Every engineer has a starting point. Mine was a simple question: "What if AI could help farmers save their crops?"
I started where most AI engineers start — with curiosity. But curiosity without execution is just daydreaming. So I built things. Real things that real people use.
My first serious project wasn't for a grade — it was for farmers. A potato disease classification system that could identify crop diseases with 92% accuracy, optimized to run on mobile devices in areas with limited connectivity. That's when I realized: the best AI isn't the most complex — it's the most useful.
From there, I went on to build production ML systems for real estate price prediction deployed on AWS, multi-agent AI systems that generate working code, and GenAI-powered tools for automated outreach. Each project taught me the same lesson — the gap between a notebook and production is where the real engineering happens.
Now, with an MSc in Artificial Intelligence from UWE Bristol, I bring together deep academic foundations in AI ethics, bias detection, and experimental research with hardcore engineering skills in Docker, cloud infrastructure, and scalable API design. I'm not just an ML engineer — I'm a full-stack AI problem solver.