I build AI systems that run in production. Sixteen years of that, across industries that don't
tolerate failures — financial markets, healthcare, enterprise software at scale. My path into AI
wasn't through a PhD programme; it was through real engineering problems that needed solutions
that worked today.
The systems I'm drawn to sit at the boundary between research and production: retrieval pipelines
that actually surface the right thing, ML platforms that make model iteration feel like software
engineering, LLM systems that hold up under the constraints of real organisations. The hard
problems are rarely the algorithms — they're the reliability, the scale, and the integration
with everything else.
I care about engineering culture as much as I care about the systems. I've spent years running
internal AI education — not because it was required, but because I've seen what happens when a
team genuinely understands what they're building with. The gap between engineers who understand
AI and those who don't is widening. It matters.
What I bring is a combination that's genuinely rare: deep technical foundations in AI and ML,
the architectural instinct to design systems that scale, and the leadership experience to build
the teams that run them. I'm focused on the long game — not just shipping the model, but
building the platform and the engineering culture that keeps shipping.
