An AI architect decides how machine learning and generative AI fit into an organisation’s systems: which problems are worth solving, what data and platform they need, which models and services to use, how the result stays reliable, secure and compliant, and what it all costs. This track covers that whole span in six stages, each ending with an action step that builds a portfolio artefact a hiring manager can evaluate.

For senior engineers, ML practitioners and solution architects who want to own AI decisions rather than just implement them.