Artificial intelligence
AI Engineering
Move from prototypes to production. We train engineering teams to design, build, and operate AI systems that hold up in the real world — LLM applications, retrieval-augmented generation, robust evaluation, and the MLOps discipline that keeps them reliable once they ship.
Enquire about trainingOverview
Building an AI demo is easy. Building an AI system that stays accurate, reliable, and affordable once real users depend on it is a different discipline entirely — and it is where most AI initiatives stall. The gap between a promising prototype and a dependable production feature is exactly the gap this programme closes.
AI Engineering is a hands-on programme for the teams who have to ship. We work through the architecture of real LLM applications — prompting and context design, retrieval-augmented generation, structured outputs, and orchestration — and then go where many courses stop short: evaluation, guardrails, monitoring, and the MLOps practices that keep AI behaving in production.
Throughout, participants build and assess working components rather than watching slides, so the patterns transfer straight back to your codebase. Teams leave able to choose the right approach for each problem, to measure quality rigorously, and to operate AI systems that scale with usage instead of buckling under it.
Who it's for
- Software and ML engineers building AI-powered features and products
- Platform and data teams operationalising models at scale
- Technical leads responsible for AI reliability, cost, and quality
- Data scientists moving from experimentation into production delivery
- Engineering managers shaping their team's AI delivery practices
What's covered
- LLM application architecture — prompting, context design, and orchestration
- Retrieval-augmented generation — chunking, embeddings, and vector search
- Evaluation — building test sets, scoring quality, and catching regressions
- Guardrails, structured outputs, and handling hallucination and failure modes
- MLOps — deployment, versioning, monitoring, and observability for AI systems
- Latency, cost, and scaling trade-offs in production AI workloads
- Fine-tuning versus retrieval versus prompting — choosing the right approach
- Integrating AI services and models into existing application stacks
Format & delivery
- Instructor-led, hands-on, delivered on-site or live virtual
- Practical labs building and evaluating real AI components
- Cohort sizes tuned to your team — typically 6 to 16
- Multi-day delivery scoped to your stack and maturity
- Tailored to your chosen models, frameworks, and cloud on request
Outcomes
- Engineers who can ship AI features that are reliable, not just demonstrable
- A shared, repeatable approach to evaluation and quality control
- Production AI systems that are monitored, observable, and cost-aware
- Confidence choosing between prompting, retrieval, and fine-tuning
Frequently asked questions
Does this assume deep machine-learning expertise?
No. The programme is aimed at engineers building with modern AI services and models, so a solid software-engineering background is enough. We go deep on the application and operational layer rather than on training models from scratch.
Is it tied to a particular model or framework?
The principles are portable across providers and frameworks. Where you tell us your stack in advance — your chosen models, orchestration libraries, and cloud — we tailor the labs to match.
How much of the course is hands-on?
The majority. Participants build and evaluate real AI components throughout, so the skills transfer directly back to your codebase.
Do you cover evaluation and testing?
Yes, and we treat it as central. Without rigorous evaluation, AI features regress silently, so we make building test sets, scoring quality, and catching regressions a core part of the programme.
Can you cover fine-tuning?
We cover when fine-tuning is the right choice versus retrieval or prompting, and the practical trade-offs involved. The emphasis is on making the right architectural decision, not defaulting to the most complex option.
Download the datasheet
Get the full programme outline, delivery options, and example agenda as a PDF.
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