Join us for a production-grade AI engineering programme with multi-track entry, designed for learners with varying levels of experience.
Develop the engineering rigour, evaluation and governance capability, and system design judgement necessary to build AI that works in production.
Through structured learning, real-world builds and a four-week employer capstone, you'll develop the skills and the confidence required of production AI engineers.
This programme has 3 entry points. Tracks differ only by where you start and how long the journey takes. The capability you finish with, the certificate you earn and the employer capstone you complete are the same for everyone.
Go further, faster
Start date (by track):
Track 1: 26 October 2026
Track 2: 25 January 2027
Track 3: 18 January 2027
Time Commitment: 15–20 hours per week (part-time, online). Programme length is track-dependent: 36 weeks –Track 1; 28 weeks – Track 2; 16 weeks – Track 3.
Support: One-to-one career coaching with a Career Coach, plus weekly live sessions with industry practitioners.
Portfolio Development: Build applied, industry-informed projects throughout, culminating in a four-week employer capstone built to a real employer brief.
Payment: Fees are track-dependent, varying from £5,495 to £8,495. You can benefit from a reduced rate when you pay upfront before the programme starts. Flexible payment options are available.
Multi-track entry
3 entry points based on experience and capability, not one route with a high bar. Every learner starts where their skills genuinely are and reaches the same outcome: a job-ready AI engineer.
Comprehensive curriculum
Covering RAG and retrieval, multi-agent systems, LLMOps, AI security hardening, responsible AI governance, inference cost engineering and stakeholder communication, ensuring you can architect, build, evaluate, govern and stand behind production AI systems, not just use them.
Hands-on employer-facing capstone
Every track converges on a four-week employer capstone: architecture and scoping, build sprints, and a live Demo Day with employer partners, Q&A included. You're assessed both individually and as a team, so what you can do is unmistakably your own, and you leave with a public portfolio of production-quality artefacts, not a case study.
Learn from academics and practitioners
University of Cambridge faculty set the academic standard, alongside industry practitioners who keep the curriculum current through guest sessions and masterclasses.
One-to-one career coaching, with a Career Milestone Guarantee
A career coach works with you from week one to set your goal and plan your route, with a dedicated success manager and facilitators supporting you throughout. Under the Career Milestone Guarantee, coaching continues until you hit a meaningful career milestone; if you haven't by the time you complete, coaching extends another six months, free (subject to eligibility: completing all coursework, submitting portfolio projects, attending coaching sessions and documenting job-search activity).
Industry mentorship
Benefit from regular live sessions with experienced cybersecurity leaders and practitioners, offering insight into current challenges, leadership decision-making and best practices across industries.
Flexible learning
Online, 15–20 hours a week, mostly asynchronous, with live faculty sessions scheduled outside standard working hours and always recorded. This programme is built for people who can't pause their career to upskill.
Cambridge credential
A Cambridge PACE certificate of completion, regardless of which track you entered on.
Whatever background you're coming from, what unites our learners isn't a shared starting point, it's a shared goal: the ability to engineer AI systems that work.
Aspiring AI engineers
For capable, motivated professionals who cannot yet code confidently, whether you bring deep experience in another field, you're changing careers, or you're early in your working life. If you're ready to build AI applications that solve real problems this programme is for you.
Developers ready to specialise in AI
For data scientists, junior developers, self-taught coders and analytics professionals who can already write working code, often Python, but haven't yet built production software systems or the AI-specific engineering depth the market now requires.
Software engineers moving into AI systems
For practising software engineers with production experience who haven't yet built production AI systems incorporating LLMs, RAG pipelines, multi-agent architectures or AI-specific governance.
RAG and Retrieval Architecture
Design retrieval systems that actually hold up under real use, not just in a demo. Learn to architect, evaluate and harden retrieval pipelines against the failure modes that quietly degrade performance at scale.
Multi-Agent Systems
Build systems that orchestrate multiple AI agents working together. Understand tool use, coordination, and the specific failure modes agentic systems introduce that single-model applications don't.
LLMOps and Production Evaluation
Learn to monitor, evaluate and maintain AI systems once they're live, including drift detection and the evaluation frameworks that catch problems conventional software testing misses.
AI Security and Responsible Governance
Harden AI systems against the risks specific to AI, and apply responsible AI principles in practice, to a standard you can document and defend, not just describe.
Inference Cost Engineering and Stakeholder Communication
Build systems that are affordable to run at scale, not just functional, and develop the ability to explain technical trade-offs to the people who have to act on them.
View the full curriculum breakdown by requesting the programme brochure.