AI expertise, built on two decades of operations judgement.
The rare combination: someone who knows what breaks in production and has been building with LLMs since before it was a category.
I'm a systems architect and AI-native engineer with nearly two decades across IT operations, DevOps and cloud — and I've been building hands-on with large language models since GPT-3 early access, four years before most teams started.
I architect like someone who's been paged at 3am: I care about isolation, scaling, cost and what actually breaks in production — not just what demos well. Recently I designed and built, solo, a secure multi-tenant platform end to end: schema-per-tenant data isolation, RBAC/ABAC access control, an AI app-generation system (plain-English brief → working app), safe sandboxing of untrusted code, and a single AI gateway for model governance. I took the core to production in about a month and evolved it from there.
The difference isn't the tools — it's the judgement underneath them. Twenty years of operations is why the systems I build hold up: I know how compute and storage scale under load, how multi-tenant isolation fails, and what an architecture really costs over three years rather than three weeks.
AI-native development is also leverage. I run several workstreams in parallel — the output of a small team, with one owner's architectural judgement on every branch. And I'm deliberately not wedded to any stack, language, product or tool: I pick what's right for the problem and keep hunting the next, better way to build. The tools are interchangeable; the judgement is the constant.
I'm always in a state of discovery, and right now I'm most energised by the intersection of deep cloud/ops experience and AI-native software design — and by helping teams and whole organisations adopt the way of working I've spent four years refining.