I build practical AI products — and the backends behind them.

I'm a Computer Science (AI) graduate with a backend and data focus. I like the unglamorous parts — modelling the data, designing the APIs, wiring up auth and business logic — because that's where products actually become reliable. Most of my work lives at the intersection of machine learning and the systems that put it to use.

What sets my work apart is the combination of CS and finance. I'm pursuing an MSc in Finance alongside engineering, so I read a problem on two levels at once: the operational data model and the financial logic behind it. That makes me comfortable building analytics, financial and operational tooling, and dashboards that have to be correct, not just pretty.

Above all I have a bias toward shipping. I'd rather have a working MVP in front of real users than a perfect plan — then iterate from there. I've taken products from a blank repo to something investors and hackathon judges paid attention to, and I care about doing that calmly and end to end.