Artificial intelligence (AI) engineer

Career path and progression

With experience, you could:

• move into a more senior role
• take responsibility for your team or a project
• set up your own business
• work as a consultant

You may find opportunities to work internationally by travelling overseas or working remotely.

What it takes

Skills and knowledge

You'll need:

• maths knowledge
• analytical thinking skills
• to be thorough and pay attention to detail
• knowledge of engineering science and technology
• the ability to write computer programs
• the ability to come up with new ways of doing things
• knowledge of systems analysis and development
• persistence and determination
• to have a thorough understanding of computer systems and applications

Related subjects

Most relevant

• Computer Science - AI engineers write code every day using programming languages like Python and SQL to build machine learning models and algorithms. They need a deep understanding of data structures, algorithms, and computational thinking to develop software that can learn from data and solve complex problems.
• Mathematics - AI engineers rely heavily on linear algebra, calculus, probability, and statistics to build and train machine learning models. Understanding mathematical concepts is essential for designing algorithms that can recognise patterns, make predictions, and improve over time.

Also relevant

• Physics - AI engineers benefit from the mathematical modelling and problem-solving approaches taught in physics. Concepts like optimisation, systems behaviour, and signal processing – which are rooted in physics – appear regularly in machine learning and robotics applications.
• Business - AI engineers work closely with businesses to understand the real-world problems they want to solve with AI. Understanding how organisations operate, make decisions, and measure success helps them design solutions that genuinely add value rather than just being technically impressive.
• Engineering - AI engineers apply engineering principles when designing, building, and testing AI systems – from prototyping machine learning models to programming robotic systems in manufacturing. A systematic approach to problem-solving and understanding how complex systems work is central to the role.
• Biology - AI engineers working in healthcare develop tools like disease screening programs and medical imaging analysis. Understanding biological systems and how diseases work helps them build AI models that can accurately detect patterns in medical data.
• English Language - AI engineers need to communicate complex technical ideas clearly to non-technical colleagues and clients. They write reports, present findings, and explain the risks and benefits of AI solutions – all of which require precise and accessible language.