Entry-level AI jobs in the UK and how graduates land them 

If you want to work in AI but don’t have a technical background, you might assume you need specialist experience to get started. That isn’t always the case. 

Your first step towards an AI career could be through software development, data analysis or a graduate technology programme. UK research into AI-related vacancies has identified software engineers and data analysts as roles that use technical skills to implement AI solutions, alongside more specialised positions such as data scientists and machine learning engineers. 

This means you don't necessarily need a job with 'AI' in the title to start building relevant experience. 

A foundation in computer science and artificial intelligence can help you develop the programming, data and problem-solving skills used across these roles. In this guide, we'll look at some entry-level routes and how to prepare for them. 

What does a career in AI look like? 

Working in AI doesn't always mean building machine learning models. AI-related work also includes implementing technology, analysing data, developing software and helping organisations introduce AI into their operations. 

The right starting point depends on what you already know and what you want to do. If you enjoy coding and building applications, software development may be a good fit. If you're more interested in finding patterns in information, data analysis could be a better starting point. 

There are also more specialised paths, including data science and machine learning, as well as roles that combine technology with business knowledge. 

Your first job doesn't have to define your whole career. Use it to build technical experience and find out which areas of AI and computer science interest you most. 

What are some entry-level AI jobs in the UK? 

Here are some of the roles you might find when exploring AI-related careers. 

Junior software developer 

If you enjoy coding and want to understand how digital products are built, software development can provide a route into AI-related work. 

As a junior software developer, you'll build skills in programming, testing and debugging while learning how different parts of an application work together. 

Software engineers were identified by UK government research among roles requiring technical skills to implement AI solutions. The technical skills you develop in software development can also prepare you for work on AI-enabled projects. 

Useful skills to build: programming, testing, debugging, logical thinking and software development principles. 

Junior data analyst 

If you enjoy working with information and spotting patterns, data analysis could be  a useful starting point for an AI-related career. 

You might work with datasets, look for trends and turn your findings into information other people can use. Data analysts were among the roles identified in UK government research on jobs requiring technical skills to implement AI solutions.  

Useful skills to build: data handling, databases, analytical thinking and communicating findings clearly. 

If you're moving from finance, healthcare, retail or another data-heavy sector, your existing knowledge could also help you understand the problems behind the numbers. 

Graduate data scientist 

If you want to specialise further in data, you may also come across graduate data science roles. 

Data science uses statistical and machine learning techniques to analyse data and answer questions. Data scientist was one of the common job titles identified in UK research into highly technical AI roles. 

However, check the requirements of individual vacancies carefully. The same government research found that some highly technical AI roles had demanding qualification requirements, so not every position advertised as a data scientist will necessarily be aimed at someone starting their career.  

Useful skills to build: statistics, data analysis, programming, logical thinking and, depending on the role, machine learning. 

Junior machine learning engineer 

If you're particularly interested in how machines learn from data, machine learning engineering is a more specialised path you could work towards. 

The machine learning engineer appears among the common, highly technical AI occupations identified in the government's analysis of UK job vacancies.  

Because requirements vary between employers, look closely at job descriptions before deciding which skills you need. You'll typically need a foundation in programming, data and mathematics for this type of work. 

Useful skills to build: programming, algorithms, data, mathematics and machine learning concepts. 

AI and automation roles 

Not everyone working with AI develops models. 

Some roles focus on applying AI to existing processes and helping businesses decide where the technology could be useful. 

This could suit you if you're interested in technology but also like solving practical business problems. Experience in another industry can be valuable here, as you’ll already understand some of the problems and processes that organisations in that sector face. 

Graduate technology consultant 

Technology consultants help organisations understand problems and decide how technology could help solve them. 

This route can suit you if you enjoy technology but also like working closely with others. You might be asked to understand a client's problem, help evaluate possible solutions and communicate technical information to colleagues or customers. 

It can also be worth exploring if you're changing careers. Experience in finance, healthcare, education, retail or another field doesn't disappear when you move into technology. You can combine that understanding with the technical skills you're developing. 

What skills do you need for an entry-level AI career? 

You don't need to know everything about AI before applying for your first role. Instead, focus on building a few core skills. 

• Programming: Learn how code works and practise writing, reading and debugging it. 
• Data: Understand how information is collected, organised, stored and analysed. 
• Problem-solving: Practise breaking a larger problem into smaller steps and deciding how technology could help solve it. 
• Maths and logical thinking: These can become particularly important if you want to move into areas such as machine learning or data science. 
• Communication and teamwork: You'll often need to explain technical information, understand what someone needs and work with people who have different areas of expertise. 

Rather than trying to master every new AI tool, concentrate on understanding how technology works and how to solve problems with it. Focus on skills you can apply across different tools and platforms, rather than becoming dependent on one AI product. 

Do you need an AI degree to work in artificial intelligence? 

No. A specialist AI degree isn't the only route into the field. 

If you've been searching for courses in computer science with artificial intelligence, or perhaps a computer science and artificial intelligence degree, you may have found programmes specifically focused on both areas. 

A broader computer science degree is another option. 

Computer science can introduce you to areas such as programming, algorithms, data and software development before you decide whether you want to specialise further through projects, work experience or further study. 

Aston University online's BSc Computer Science, for example, covers programming alongside artificial intelligence and machine learning. Explore the online BSc Computer Science. 

So rather than choosing a programme based only on whether 'AI' appears in its title, look at the curriculum. Ask yourself whether it will help you build both the broader computing skills and the specialist knowledge you're interested in. 

How can you prepare for an AI career while studying? 

A degree can help you build your knowledge, but employers will also want to understand what you can do with it. You can start building evidence of your skills before you graduate. 

Build projects you can show employers 

Create something that lets you put your knowledge into practice. 

Depending on your experience, you could analyse a dataset, build an application or experiment with a simple machine learning project. 

Don't focus only on the final product. Be prepared to explain what you were trying to achieve, why you chose your approach, what went wrong and how you solved problems along the way. 

Get comfortable writing and debugging code

AI tools can generate code, but you still need to understand programming. 

Practise reading, writing, testing and debugging code yourself. If an AI tool produces code for you, make sure you understand what it does rather than copying the answer without checking it. 

Learn to question AI 

Knowing how to use AI is useful. Knowing when its output is wrong matters too. 

When an AI tool suggests some code or a solution to a problem, read it carefully. Test the result. Look for errors and consider whether you'd solve the problem differently. 

The aim is to use AI alongside your technical knowledge, not as a substitute for it.  

Look beyond permanent jobs for experience 

Your first practical experience doesn't have to come from a permanent AI position. 

Internships, graduate programmes and personal projects give you opportunities to apply what you've learned and examples to discuss in job applications and interviews.  

Use the experience you already have 

If you're changing careers, don't assume you're starting again from zero. 

Perhaps you understand how patient information is used in healthcare, how customers behave in retail or how financial data supports business decisions. 

That experience still matters.  Adding technical skills can help you apply what you already know in a new context. 

Why study computer science if you want to work with AI? 

If you've been asking, "Why should I study artificial intelligence?" or “How can I study artificial intelligence?", it helps to look at what's behind the technology. 

AI systems involve more than knowing how to use an AI tool. Depending on what you want to do, understanding programming, algorithms, software and data can help you understand how those systems are built and how they behave. 

This is where computer science and AI connect. 

A computer science degree can give you the opportunity to study different areas of computing before deciding where you'd like to focus. You might begin your degree interested in AI and discover that you particularly enjoy software development, cybersecurity, data or another field. 

This can also be useful if you're a self-taught programmer who wants to structure your existing knowledge, or a career changer who needs a starting point. 

Aston University Online's BSc Computer Science covers programming, artificial intelligence and machine learning. The programme is delivered online over four years part-time and is BCS accredited. 

Start building your computer science skills 

There's no single route into AI, and you don't need to know exactly where you want to specialise before you begin. 

You might start in software development, build experience in data or work towards a more specialised machine learning role. What matters is understanding what different careers involve and building the skills required for the direction you want to take. 

If you're looking to move into technology, formalise skills you've taught yourself or start learning computer science from the beginning, Aston University online's BSc Computer Science provides a flexible, part-time route to developing your computing knowledge. 

Explore the online BSc Computer Science.