Generative AI for engineering
Enterprise use cases, model selection, and where generative systems genuinely reduce delivery cost.
Audience track
AI-Powered Professional
Do not replace your engineering experience. Add AI to it and stay ahead of your own market. Built for working functional, QA and automation engineers who already know how software gets delivered.
Positioning
Your domain experience is the asset. AI is the multiplier.
Ten years of knowing how a payments platform actually breaks is not something a graduate with an AI certificate can replicate. What you are missing is the vocabulary and the engineering practice to build AI systems on top of that knowledge — and to say, credibly, when an AI system should not be trusted.
This track transforms existing QA, automation or functional testing skills into AI-powered engineering capability: AI test generation, automation generation, test-data generation, defect analysis, self-healing automation, agents, RAG, evaluation and autonomous testing.
Who this is for
- Functional QA engineers
- Automation engineers
- SDETs
- Test leads
- QA managers
- Business analysts
- Software engineers
- DevOps engineers
- Data engineers
- Technical analysts
Core learning areas
What the track covers
Prompt & context engineering
Structured outputs, system prompts, context windows and the discipline of making models behave predictably.
Enterprise RAG & knowledge assistants
Grounded retrieval over your own documentation, with evaluation gates that stop hallucinated answers.
AI agents & domain automation
Agents applied to your specialism, with planner, executor and reviewer roles and human approval.
AI evaluation & quality engineering
Faithfulness, groundedness, hallucination detection, golden datasets and automated regression for AI.
AI guardrails & security
Prompt injection, jailbreaks, PII protection, output validation and OWASP LLM security concepts.
Engineering foundations
Python, FastAPI, APIs, Git, Docker, PostgreSQL and Redis — enough to ship, not to specialise.
Orchestration, MCP & A2A
Agent orchestration, the Model Context Protocol and agent-to-agent interoperability.
AI-powered testing track
Applied directly to quality work
- AI-generated test cases
- AI test-data generation
- AI API testing
- Self-healing automation
- AI visual testing
- Accessibility testing
- AI performance testing
- AI defect analysis
- Root-cause analysis
- Test prioritisation
- Regression optimisation
- Autonomous testing agents
Suggested stack: Playwright · Selenium · WebdriverIO · REST Assured · Karate · Postman · k6 · Axe · Jira · Azure DevOps · GitHub · LangChain · LangGraph
Domain-based capstone
Build the agent your own job needs
Your capstone is chosen to match your professional background, so the work is immediately demonstrable to your current employer as well as your next one.
| Your background | Suggested capstone |
|---|---|
| QA / Automation | Autonomous QE Agent |
| Business Analyst | Requirement Intelligence Agent |
| DevOps | Incident Investigation Agent |
| Data Engineer | Data Quality Agent |
| HR / Operations | HR Knowledge & Policy Agent |
| Finance | Financial Document Intelligence Agent |
Career outcomes
Where this track leads
- AI-enabled QA Engineer
- AI Automation Engineer
- SDET with GenAI
- AI Business Analyst
- AI Developer
- AI Solutions Engineer
- AI Test Architect
- AI Quality Engineer
- AI Consultant
Post-training support includes job preparation, mock interviews, resume and portfolio guidance, domain-specific implementation support and real-project guidance.
Questions
For working professionals
The flagship goes deeper into AI engineering and targets a full career transition into AI-native roles. This track is calibrated for professionals who want to add AI capability to an existing specialism and lead its adoption in their current organisation. Content overlaps substantially; the capstone and career framing differ.
Yes, and this is the track built for you. The AI-powered testing content starts from test design and generation rather than assuming a mature automation framework. You will still write code, but you will not be expected to arrive fluent in it.
It does. Requirement intelligence agents, grounded document retrieval and evaluation are directly relevant, and there is a BA-specific capstone brief. Several of the strongest capstones we see come from analysts, because they bring the domain problem with them.
Nothing does. But the engineers being squeezed right now are the ones whose only differentiator was executing scripted work. The ones being promoted are the ones who can evaluate an AI system honestly and tell their organisation when to trust it. That is what this track builds.
Next cohort
Add AI to the experience you already have.
Talk to admissions about how this track maps onto your current role and domain.
Cohort snapshot35 seats
01100 days live + self-paced15 wks
02Mentor-led weekend classes10 hrs/wk
038 guided projects + capstoneportfolio
04Interview prep & career supportongoing
Weeknight office hours · lifetime access to recordings · one accountable mentor across all 100 days