AI

AI Engineer

Structured, foundational training across the AI/ML lifecycle — model foundations, deep learning, ethics, and deployment — for learners building toward a general AI engineering role.

50
hours of live training
10 weeks
at one hour a day, weekdays
4
modules
24
topics covered

Before you start

Some technical background helpful. If you are not sure whether that describes you, the consultation exists to answer exactly that — and to tell you if a different programme is the better start.

Price

United Kingdom
£750
Canada
$1,750

Tax included — the price shown is the price you pay. This covers the training stage. The bootcamp and placement stages are priced separately.

Curriculum

A module-by-module breakdown

Everything covered in the program, in the order you'll cover it.

01Python & ML Foundations

12 hours

The engineering base for AI work

  • Python packaging & typing
  • NumPy, pandas & data structures
  • scikit-learn estimators & pipelines
  • Supervised & unsupervised methods
  • Evaluation metrics & validation
  • Reproducible experiment setup

02Deep Learning with PyTorch

15 hours

Build and train real networks

  • Tensors, autograd & training loops
  • CNNs for vision tasks
  • Transformers for text
  • Transfer learning & fine-tuning
  • GPU training & mixed precision
  • Debugging convergence problems

03LLM Application Engineering

8 hours

Wire models into software

  • Prompt design & structured outputs
  • Function calling & tool use
  • RAG with a vector database
  • Streaming & async API patterns
  • Evaluation of generated output
  • Guardrails & input validation

04Deployment & MLOps

15 hours

Run it in production

  • FastAPI model services
  • Docker & Kubernetes deployment
  • MLflow tracking & registries
  • CI/CD for model releases
  • Monitoring, drift & rollback
  • A capstone AI service end to end

Projects

Five projects, in AI Engineer

The bootcamp runs the same five stages whatever you enrolled in, so the portfolio you finish with is deep in one technology rather than shallow across several. Each is scoped, designed, built and reviewed.

  1. 01 · Foundations

    A first build that puts the core concepts to work — environments configured, the basic workflows exercised, the fundamentals proven rather than assumed.

  2. 02 · Multiple moving parts

    A larger solution with several components and an integration or two, applying the practices that are conventional in your stack rather than improvising.

  3. 03 · Architecture that scales

    A design-led build: scalability, monitoring and the production-grade patterns your field actually uses, decided before they are implemented.

  4. 04 · An enterprise scenario

    A realistic situation with services that depend on each other, security to harden and performance to tune — the kind of work that is waiting on the other side of the offer.

  5. 05 · Capstone

    End to end, everything together: planned, designed, built, deployed and documented well enough to sit at the top of your GitHub profile.

What's included

Everything in the programme

The same seven things in every programme on the site — the subject is what changes.

  • Live training

    Taught live by a practitioner, not pre-recorded video you work through alone.

  • 50 hours

    Per programme as standard — 60 for IT Support Analyst, 70 for the AI Consultant and cloud engineering tracks.

  • Projects

    Five briefs in your own stack, each scoped, designed, built and reviewed the way a delivery team would.

  • Mentoring

    One mentor who stays with you for the whole programme, rather than whoever is free that week.

  • Resume & LinkedIn

    Both rewritten around the work you actually built, and positioned for the roles you are targeting.

  • Interview prep

    Mock interviews on the technical and the competency side, including defending the projects you built.

  • Placement support

    Your profile put in front of employers, and the search continues until you are hired.

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