This programme is designed for QA professionals with at least one year of professional testing experience — manual or automation — who want to transition into AI-adjacent roles such as AI QA Engineer, ML Quality Engineer, LLM Tester, or AI Product Analyst. It is a career transition programme, not an introductory QA course.

No prior ML experience is required. The programme builds ML knowledge from the ground up using your existing QA thinking as the foundation. Module 2 covers the key concepts — training, evaluation, bias, data quality — explained specifically for testers without requiring deep mathematics or statistics.

Basic Python is strongly recommended — comfortable with variables, functions, and loops at minimum. Module 3 covers Python specifically for ML quality work and builds on that foundation. Learners who join without any Python background will find Module 3 significantly harder and are advised to complete ITLearnner's Python for Beginners course first.

The programme prepares learners for AI QA Engineer, ML Quality Engineer, LLM Tester, AI Product Analyst, and LLMOps roles — all of which are in growing demand as organisations build and deploy AI systems. The career transition module explicitly maps these roles to the skills built across the programme and prepares your CV and portfolio for each.

Your QA experience is the starting point of this programme, not something to work around. The entire first module is dedicated to mapping your existing skills — test case design, defect analysis, regression strategy, risk assessment — to the equivalent responsibilities in AI and ML teams. QA professionals enter this programme with more relevant experience than they realise.

LLM testing involves evaluating the output of large language models — like GPT and Claude — for accuracy, consistency, bias, and harmful content. It is a rapidly growing specialisation that sits squarely within the QA skill set. Module 4 covers LLM evaluation frameworks and prompt engineering techniques that allow QA engineers to take ownership of AI output quality in product teams.

Yes — portfolio development is built into the programme structure. You will produce at least three AI quality artefacts: a data quality validation report, a model evaluation notebook, and an LLM test suite. Module 5 covers how to position these artefacts on your CV and GitHub profile for AI-adjacent hiring managers.

The programme runs across 20 two-hour live sessions — 40 hours of content in total. Sessions are delivered in small groups with a dedicated ITLearnner tutor, and all sessions are recorded. The programme is designed to be completed alongside full-time work with one or two sessions per week.

Yes — Module 5 includes a dedicated mock interview section covering the most common AI QA interview questions and how to answer them from a QA background. The module also covers how to talk about your QA-to-AI transition story in a way that resonates with AI hiring managers who may not have a QA background themselves.

Learners have access to their ITLearnner tutor by email between sessions, and the small group format means individual questions are addressed in every live session. Career support including CV review, LinkedIn profile feedback, and job search strategy is available as part of Module 5. Session recordings are available throughout the programme.