20
Live Sessions
120 mins
Session Length
Applied Learning
Level
Professionals
Suitable For
Live Online
Delivery
UK Time
Time Zone
LEARNING OBJECTIVE
By the end of this course, you will be able to
Build data quality checks and model evaluation scripts in Python that map directly onto your existing QA testing instincts.
Present a portfolio of AI quality artefacts that translates your QA background into evidence an AI hiring manager understands.
Justify a career transition plan, covering CV rewriting and portfolio positioning, that names the specific AI-adjacent role you are targeting.
Configure prompt engineering workflows to design, test and evaluate LLM outputs as part of an AI quality pipeline.
Lead a conversation about where QA expertise fits inside an ML or LLMOps team, not just where it used to fit.
TARGET LEARNER
Who this course is for
For QA engineers ready to stop wondering if AI will replace their role and start building a career inside it.
QA engineers with at least a year of professional experience who want to move into AI quality or ML engineering roles.
Manual and automation testers who see AI reshaping QA and want to get ahead of the transition, not react to it.
Senior QA professionals who want to reposition their testing expertise as a competitive advantage in AI teams.
QA engineers in organisations undergoing AI transformation who need to upskill to stay relevant on their team.
If you see yourself in one of these, this course is built for you.
Prerequisites
What you need
Professional QA or testing experience is required before joining this programme.
- A minimum of one year of professional QA experience — manual or automation — is expected.
- Basic Python knowledge is helpful and strongly recommended before the programme begins.
- A laptop with Python installed and a stable internet connection are required.
- A genuine motivation to transition into AI-adjacent roles is expected — this is a career programme, not a short course.
- No prior machine learning or AI engineering experience is required.
COURSE OVERVIEW
What this course is about
QA-to-AI Career Transition Program is a structured, instructor-led programme for QA professionals with at least one year of experience who want to move into AI and ML-adjacent roles. The course covers machine learning fundamentals, Python for ML quality, prompt engineering, and career transition planning in depth, combining conceptual grounding with real Python exercises throughout every session.
The programme runs across 20 two-hour live sessions, progressing through your existing QA skills mapped to AI roles, and machine learning fundamentals before finishing with Python for ML quality, prompt engineering and LLM testing, and a dedicated career transition module. Every topic is taught from the perspective of a QA professional making the transition, not a graduate starting from zero.
A laptop with Python installed provides full access to the exercises throughout the course. Portfolio-building tasks are integrated from session one so learners are producing AI quality artefacts throughout, not just in the final weeks.
What the course covers:
QA to AI Mapping: how existing test design, defect analysis, and regression strategy map onto AI and ML roles.
Machine Learning Fundamentals: model training, evaluation, bias, and data quality from a QA perspective.
Python for ML Quality: data quality checks, model evaluation scripts, and basic ML pipeline tests.
Prompt Engineering and LLM Testing: designing, testing, and evaluating LLM outputs.
Career Transition: portfolio positioning, CV rewriting, and interview preparation for AI-adjacent roles.
CIRRICULUM
5 Modules. 20 live Sessions. Hundreds of small wins.
The curriculum starts by mapping your existing QA skills onto AI roles, then moves through machine learning fundamentals and Python for ML quality before finishing with prompt engineering and a dedicated career transition module.
01
Your QA Skills in the AI World
Map your existing QA expertise (test design, defect analysis, regression strategy, risk thinking) to the equivalent roles and responsibilities in AI and ML teams, and understand exactly where the demand for QA skills in AI organisations is growing.
02
Machine Learning Fundamentals for QA Professionals
Build a solid conceptual foundation in machine learning (training, evaluation, bias, and data quality) explained through the QA lens you already have, without requiring deep mathematics.
03
Python for ML Quality
Learn the Python skills specifically needed for ML quality work (data validation with pandas, model evaluation scripting, and pipeline testing) applied immediately to real datasets and model outputs.
04
Prompt Engineering and LLM Testing
Understand how large language models work from a tester's perspective, apply prompt engineering techniques, and build a structured approach to evaluating LLM output for accuracy, consistency, bias, and harmful content.
05
Portfolio, CV and Career Transition
Build a portfolio of AI quality artefacts, reposition your QA CV for AI-adjacent roles, prepare for technical interviews from a QA background, and leave with a personalised 90-day career transition plan.
Course Types
We offer three structured learning paths based on your goals:
Crash Course (Fast-Track)
Quick, intensive courses designed to teach specific skills efficiently. Ideal for those upskilling fast or preparing for certifications.
DeepDive Program (Full Mastery)
Comprehensive, step-by-step learning for full mastery. For beginners and professionals seeking long-term, deep expertise.
MentorConnect (One-on-One)
Personalised mentorship with real-world guidance. Best for those who thrive with direct, expert-to-learner coaching.
Learning Approaches
We recognize that everyone learns differently, so we offer flexible learning formats to fit your needs:
One-on-One Training
Personalized, instructor-led coaching tailored to your learning speed.
Best for career-specific coaching or specialized training needs.
Small Batch Classes (2-5 learners)
Interactive, discussion-based learning in small groups.
Encourages collaboration, teamwork, and peer-to-peer engagement.
FAQ
1
Who is this programme designed for?
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.
2
Do I need to know machine learning before starting?
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.
3
How much Python do I need before joining?
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.
4
What roles can I move into after completing this programme?
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.
5
How does this programme value my existing QA experience?
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.
6
What is LLM testing and why is it relevant to QA?
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.
7
Will I build a portfolio I can show to employers?
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.
8
How long is the programme and how are sessions structured?
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.
9
Does the programme include interview preparation?
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.
10
What support is available during the programme?
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.
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