I build the engineering around AI. Not the models themselves: the infrastructure they run on, the data platforms that feed them, and the delivery pipelines that get them into production and keep them there. A model that works in a notebook is a starting point. Everything between that notebook and a system an organisation can actually operate is the part I do.
That gap is where most AI projects stall, and it is an engineering problem rather than a modelling one. A demo answering one question is a weekend's work. The same idea serving a continent, under load, with cost ceilings, data residency rules, and an audit trail, is what the team inherits on Monday.
☁️ The infrastructure AI runs on. Cloud architecture, infrastructure as code, containers and orchestration, serverless and event-driven designs. Networking, identity, cost, and security handled up front rather than retrofitted. Systems people can hand off, not just stand up.
🧱 The data platform underneath it. Lakehouse and warehouse design, ingestion and ETL pipelines, governance and access control. Making an organisation's data reachable, trustworthy, and cheap enough to actually use.
🚀 The path to production. CI/CD and GitOps, environment and release strategy, observability, and evaluation wired into the pipeline rather than run by hand. Turning a prototype into something deployable, monitorable, and reversible.
Consulting, but hands-on. I spend as much time on architecture decisions and their trade-offs as I do in the codebase, and I lead engineering teams rather than hand over a slide deck and leave.
Always open to a conversation about complex infrastructure problems.