The disciplines

Fourteen areas, each chosen because it is hard to judge from a CV. That difficulty is the reason the register exists, so each page says plainly what makes the assessment difficult rather than pretending it is simple.

• AI strategy

  Decides what a business should and should not build with AI, and in what order.

• AI engineering

  Builds the systems that put models into production and keeps them there.

• LLM applications

  Designs and ships products where a language model is the core of the product.

• Agents and retrieval

  Builds agentic workflows and retrieval systems that return the right thing.

• MLOps and platform

  Owns the pipelines, deployment, monitoring and cost of models in production.

• Data engineering for AI

  Gets the data into a shape a model can actually use, reliably and repeatedly.

• AI governance

  Builds the policies, records and controls that let an organisation show how its AI is run.

• AI risk and compliance

  Maps obligations under regimes such as the EU AI Act and ISO 42001 onto what a business is really doing.

• Safety and evaluation

  Designs the tests that say whether a system is good enough to ship.

• Red teaming

  Attacks a system deliberately to find how it breaks before someone else does.

• AI product management

  Decides what to build, for whom, and how to tell whether it worked.

• Fine-tuning and adaptation

  Adapts existing models to a specific domain, and knows when not to.

• Training and enablement

  Gets a workforce genuinely capable with AI tools, rather than merely trained.

• Applied research

  Works on problems where the method is not settled yet.