AI Tech Institute is a boutique, practitioner-led school for the part of AI that most courses skip: taking a model out of the notebook and making it something an organisation can actually depend on. Founded in Australia in 2024, funded and directed by the person who still teaches every cohort.
Anyone can train a model in a notebook. Far fewer can take it from a prototype that impresses in a demo to a system a business depends on — packaged, evaluated, governed, monitored, and safe to hand to somebody else.
That distance is where Australian AI work keeps stalling, and it is not a knowledge problem. It is judgement, and judgement is craft: learned from someone who has done it, on work that matters, with a real deadline and someone asking hard questions at the end.
So the institute was built the way craft is actually taught. Small rooms. A senior practitioner in front of them. Your own work rather than a sample problem. And a defence at the end, because being questioned on something you built is the moment it stops being theory.
Twenty years building and leading AI, machine learning and data teams in Australian industry — and twenty-one years teaching. He founded, funds and directs the institute, and currently leads every cohort personally.
He leads asset performance and analytics at Synergy, Western Australia's state-owned electricity generator and retailer. Before that: GenAI and MLOps program delivery at EY, on platform modernisation for tier-one Australian banks; Chief AI Engineer at Hancock Prospecting, where he built enterprise GenAI for executive decision support, led a Snowflake migration, stood up on-premises NVIDIA H100 infrastructure and Kubernetes for production AI, and built a team of twelve; and nearly five years as Tech Lead for ML and Data Science at Woodside Energy.
He is an Adjunct Associate Professor at the University of Western Australia, teaching quantitative analysis and decision-making at master's level. He holds a PhD, Databricks University Alliance Faculty status and the AWS Certified AI Practitioner certification, and has published peer-reviewed research in Exploration Geophysics on machine learning for reservoir characterisation. He writes and records openly about the parts of AI engineering most courses skip — agent loops, termination logic and failure modes.
Three numbers, each with its source and sample attached — the same standard we hold ourselves to on the outcomes page.
And 88% say they cannot find candidates who have it. The demand is no longer speculative; it is written into job descriptions.
Up two points on the previous quarter, against roughly 43–44% reporting some level of adoption. More tools, not more capability.
Transparency practices and formal complaint processes lag further still — the gap between internal safeguards and external accountability.
The shortage isn't people who can prompt a model. It's people who can be trusted with one in production.
Every program ends in something real — a deployed service, an evaluated model, an agent, a written plan — built on your own work and defended in front of the room.
The reasoning that survives the next framework release: what to build, how to tell whether it works, where it breaks, and what to do before it does.
The National AI Centre's six essential practices in terms you can apply — documentation, accountability and the evidence an enterprise review actually asks for.
Named faculty, reachable after the cohort ends. Small enough that we remember your project.
Real sessions in Australian hours with a senior practitioner in the room. If you can't ask a question mid-sentence, it isn't what we sell.
Twelve to twenty-five depending on the program. When it's full it's full — we don't add a thirteenth seat to a twelve-seat room.
You bring a repository, a dataset, a process or a decision in week one, and it is the thing you carry to the defence.
The lead instructor on every cohort is a working practitioner who has built the thing they are teaching — not a full-time trainer reading someone else's deck.
The ambition is not more students. It is a school whose graduates are recognisable by the quality of their work — and that means growing in a specific order.
Senior people who have shipped the thing they teach, added one at a time. Every instructor named, public and reachable. The teacher is the product, so the hiring bar is the business model.
Resources, energy, financial services and government — built on that sector's data, regulation and constraints rather than a generic curriculum. Depth is defensible; breadth is not.
We are pursuing registration as a nationally recognised training provider, and we publish the honest state of play rather than implying we already hold it.
As cohorts complete, completion, capstone-shipped and capability-lift figures go on the outcomes page with their definitions and sample sizes attached — so they can be checked rather than believed.
Australian hours, Australian regulatory context, and delivery into New Zealand and Singapore for teams that share it. Not a global platform — a regional institute.
Strategic decision & provider readiness
Quality & governance framework documentation
Application lodged with ASQA
Documentary & site audit
Rectification & conditions response
Initial registration confirmed
Thirty minutes, no proposal pressure. Tell us what you're building or where your team is stuck, and we'll tell you honestly whether one of our cohorts is the right answer.
