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AI & Machine Learning career pathways

Build, evaluate and ship systems that use large language models and machine learning — from prompt design and retrieval to agents and deployment.

Organisations are moving from AI experiments to AI in production. That creates demand for people who can design reliable prompts, ground models in real data, measure quality, and deploy responsibly. If you already understand a business domain, you have a head start most new graduates do not.

Pathways in AI & Machine Learning

AI Engineering

Open now

Build dependable LLM applications: prompting, retrieval, tools, agents, evaluation and deployment.

Who it suits
Analysts, developers, technical support and operations professionals who already solve problems with data or code and want to build AI-powered products.
Roles to aim for
AI Engineer, LLM Application Developer, Applied AI Engineer, AI Solutions Engineer
Core skills you build
Python programming, LLM APIs & structured outputs, Prompt design, Retrieval-augmented generation (RAG), AI evaluation & testing, Agents & tool use
Time
About 38 hours of guided work plus projects. Most people finish in 8 to 12 weeks alongside a job.

Two realistic projects for your Career Proof: self-checked on Sprint, mentor-reviewed on Accelerate. Full project briefs and the week-by-week plan come in your pathway brochure.

Specialist track: Prompt Engineering & Model Behaviour.

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How every pathway works

The same six stages apply whichever role you choose.

See the platform
  1. 01DiscoverUnderstand your starting point and your target.
  2. 02BuildDevelop the skills you need.
  3. 03PractiseWork through realistic projects.
  4. 04ProveCreate evidence and receive validation.
  5. 05ConnectMeet mentors and become visible to employers.
  6. 06TransitionMove towards interviews and your next role.

Questions about AI & Machine Learning

Other career families:

Do I need a computer science degree to move into AI engineering?

No. AI engineering roles focused on building LLM applications value practical evidence: working projects, evaluation reports and clear reasoning about trade-offs. You will need to become comfortable with Python and APIs, which the pathway covers from the start.

What is the difference between AI engineering and machine learning engineering?

AI engineering usually means building applications on top of existing foundation models — prompting, retrieval, tools, agents and evaluation. Machine learning engineering goes deeper into training, feature pipelines and serving your own models.

Is prompt engineering still a real job?

Standalone prompt engineer titles are less common than they were, but prompt design, evaluation and model behaviour work are core skills inside AI engineering, product and operations roles. We treat prompting as a specialisation within AI engineering rather than a separate career.

How much maths do I need?

For AI engineering, very little beyond comfort with numbers and basic statistics for evaluation. Machine learning engineering requires more linear algebra, probability and statistics, introduced in that pathway.

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Choose your target role, then book a free 10-minute call with a mentor.

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