Profile
Applied AI engineer who takes LLM products from idea to live, reliable and secure — and owns every layer in between. I build retrieval-augmented (RAG) chatbots and AI tooling on Azure: integrating OpenAI, wiring up vector search, hardening against real-world abuse, and running the deployment and monitoring myself.
Behind the AI work sits over a decade in software development and third-line support — Java microservices and cloud backends built inside large engineering teams as well as on solo projects, plus the kind of production firefighting that teaches you to build things that don't fall over.
Underneath all of it, decades of hands-on systems administration: Linux and Windows Server, self-hosted services, networking, storage and deployment pipelines. I run a substantial home-lab and a fleet of live sites and services on cloud infrastructure — so I'm as comfortable with the servers and the plumbing as with the application sitting on top.
What I build with
Applied AI / LLM
Backend & Cloud
Integrations
Also in the toolbox
Systems & Infrastructure — decades hands-on
Experience
- Built and run “Dai”, a live customer-support chatbot (Zoho SalesIQ): RAG retrieval over a Zoho Desk knowledge base, OpenAI-generated answers with confidence gating and clean human handoff.
- Hardened it for production — prompt-injection defences, internal-KB filtering, cross-org access control, PII stripping and history purge.
- Shipped supporting tools as lean Azure Functions: time logging, Stripe reporting, document parsers, CRM data tooling. Own the architecture, deployment, security and monitoring.
- Developed business-critical claims applications, including Java microservice architectures; third-line support and performance optimisation.
- Built cross-platform e-learning modules and maintained training-system infrastructure; vendor liaison.
- Ran an independent film company and freelanced in VFX — while owning all IT infrastructure, deployment, security and high-performance compute / digital-asset pipelines.
What sets the work apart
End-to-end ownership
Idea → architecture → build → deploy → monitor. I can own the whole lifecycle of a production AI feature, or slot into a team on any part of it.
Security-minded
PII, prompt-injection and access control treated as first-class from day one — not bolted on later.
Pragmatic engineering
Low-dependency, self-contained builds. Fewer moving parts means less to break and more to ship.
App to bare metal
Comfortable the whole way down — from the application code to the servers, storage and networking it runs on.