AI & LLM Systems Engineering
Production AI, not prototypes: retrieval pipelines, agentic tool-calling, natural-language querying over real data, and the evaluation harness that tells you when a release has quietly got worse.
Technology Executive · Enterprise & Solutions Architect · AI and Cybersecurity Leader
Twenty-five years building and securing systems that have to work — from hands-on software engineering through senior engineering leadership to C-level technology strategy. I design production AI, multi-cloud architecture, and security and compliance programmes, then translate the hard parts into decisions a board can actually make.
Most engagements fall into one of six areas. They overlap more often than not — an AI system that handles regulated data is an architecture problem, a security problem and a governance problem at the same time.
Production AI, not prototypes: retrieval pipelines, agentic tool-calling, natural-language querying over real data, and the evaluation harness that tells you when a release has quietly got worse.
Legacy estates moved to cloud-native without a big-bang rewrite, and over-provisioned estates right-sized. Serverless and containers where they earn their keep, not everywhere by default.
Defending web applications and APIs against automated and human attackers, and getting organisations audit-ready in regulated sectors — mapped to the framework the auditor will actually use.
Making systems that were never designed to talk to each other work together reliably — ERP and CRM modernisation, API and multi-tier design, and high-volume data exchange that fails loudly instead of silently.
A defensible boundary between what a model may do on its own and what a human signs off. I use a tiered-autonomy model that classifies every automated action, so the human-in-the-loop line is explicit and auditable rather than assumed.
For leaders who need an independent read on a build-versus-buy call, a vendor claim, a modernisation roadmap or an architecture already in flight — explained in business terms, with the trade-offs stated plainly.
I still write the code. That is the point — advice about an architecture is worth more from someone who has had to operate one at three in the morning.
Scoping, requirements across departments, and an honest inventory of what already exists. Most failed modernisations were mis-scoped, not mis-built.
Pilots validated against actual business scenarios and real data, not a demo path. Problems surface while they are still cheap.
Releases gated on measured accuracy and output quality, not just uptime. A system that is up and wrong is not working.
Runbooks and documentation so your team owns the system afterwards. The goal is to make myself unnecessary.
Not a tool list for its own sake — these are the areas where I can design, build, review and debug rather than only advise.
Whether it is an AI system you need built properly, an architecture you want a second opinion on, or a compliance deadline you have to hit — start with the problem and we will work out whether I am the right person for it.