AI engineer job profile

Considering pursuing a career as an artificial intelligence (AI) engineer? If you’ve got an educational background in computer science (or a related field) and a flair for problem-solving, you might be a good fit.

An AI engineer uses AI and machine learning to develop, program and train complex algorithms. These algorithms allow systems to carry out tasks typically requiring human intelligence, including interpreting speech and text, identifying patterns, forecasting potential outcomes, generating creative content and more.

You’re probably wondering what you’ll find in a typical AI engineer job description. Job postings in this field will likely include the following role responsibilities:

• Collecting, cleaning, labelling and preprocessing datasets
• Working with machine learning algorithms to train new models based on specifications
• Documenting data sources
• AI prompt engineering for large language model (LLM) projects
• Testing model performance
• Supporting deployed models to flag and fix potential issues

A career in AI engineering presents many opportunities for you to learn and grow, with plenty of room for specialisation and clear routes for advancement. In this section, we break down what to expect from early career roles all the way to senior positions:

A typical AI engineering career path generally starts with entry-level roles like ‘Junior AI Engineer’ or ‘Junior Machine Learning Engineer’. While your responsibilities will vary based on the specific role, your day-to-day will likely involve the following tasks:

• Researching and developing AI platforms, including model training and deployment
• Collaborating with team members to create innovative solutions
• Improving platform performance
• Staying up to date with the latest AI advancements

As you gain more experience, you’ll be able to take on more responsibility and tackle more complex projects. It’s at this stage that you may get the opportunity to progress to mid-level roles with job titles like ‘AI Engineer’ or ‘Machine Learning Engineer’.

Here are some of the daily tasks you could undertake in these mid-level roles:

• Taking more ownership of designing, developing and implementing new models
• Implementing and managing the infrastructure and workflows for continuous integration, continuous delivery (CI/CD) and continuous model training (MLOps)
• Mentoring entry-level staff members
• Tackling more complex or ambiguous projects that require advanced knowledge and experience

Once you’ve worked in a mid-level role for a while, you’ll have developed many of the skills needed to progress to senior roles, such as ‘Lead AI Engineer’, ‘AI Architect’ or ‘Senior Machine Learning Engineer’.

Here are some of the daily tasks you could undertake in a senior AI engineer role:

• Leading the design and development of AI and machine learning solutions
• Collaborating closely with business stakeholders such as data scientists, software engineers and product managers to understand project requirements and define project strategies
• Designing, building and maintaining MLOps pipelines
• Devising and implementing AI ethics and governance strategies
• Mentoring and providing technical assistance to other AI engineers
• Identify areas for improvement within systems
• Stay at the forefront of AI and machine learning research

If your goal is to eventually reach the top of the organisation, you could take up the position of Chief Technology Officer (CTO) or Chief AI Officer (CAIO). These executive-level positions give you the power to define and drive the organisation’s technology or AI strategy. For example, as CAIO, you would provide strategic leadership, technology oversight and be the main stakeholder of the organisation’s ethics, governance and compliance policies relating to AI.

So, what can you expect to earn as an AI engineer? Salaries vary depending on job seniority, responsibilities and location, but the following salary ranges provide you with a general guide:

• Entry level: £30,000 - £40,000
• Mid-level: £40,000 - £60,000
• Senior: £60,000 - £100,000+

Your actual AI engineer salary may be higher or lower depending on your specific skillset, the company, industry and location. Plus, these roles often come with attractive benefits, which may include:

• Generous pension schemes
• Equity (more common in startups)
• Flexible working arrangements (hybrid or remote)
• Professional development budgets
• Private medical insurance

You’ll need formal qualifications and real-world training to become an AI engineer. Let’s explore what this process typically involves.

You’ll need a Bachelor’s degree in a quantitative field, such as Computer Science, Software Engineering or Mathematics. Although not typically a mandatory requirement for entry-level roles, you may also decide to pursue specialisation by studying a master’s degree. The field is highly competitive, so getting a master’s degree in Artificial Intelligence could be just what you need to stand out from other applicants.

Next is work experience. AI engineering jobs are highly competitive, so it’s important that you demonstrate to employers that you have suitable work experience to succeed in the role. One route to getting the work experience you need to become an AI engineer is an internship. An internship in a relevant field will allow you to apply the knowledge you’ve learned at university, gain hands-on experience and contribute to projects under the guidance of experienced AI engineers.

Internships aren’t your only option, however. You can build relevant experience by undertaking your own personal projects. This might involve creating your own AI projects from scratch and documenting the process on platforms like GitHub and GitLab to showcase your hard skills in AI development and soft skills like problem-solving.

You can also study for professional qualifications to demonstrate your suitability for AI engineering roles. Here are a few popular qualifications and courses that are well-suited to the field:

• IBM: Access a bank of free artificial intelligence resources which explore everything from AI fundamentals to building your own chatbots.
• Microsoft Azure: Microsoft offers a free course on AI fundamentals made up of three modules: Introduction to AI concepts, introduction to machine learning concepts and getting started with Azure AI services.
• Google: Google offers various free generative AI courses to help you grasp the fundamentals and master AI prompt engineering.

Completing professional qualifications helps strengthen your CV and shows employers that you have the skills for the role and are willing to engage in continuous learning.

You’ll need a mix of hard and soft skills to succeed as an AI engineer.

• Programming languages. You need a strong background in various programming languages to become an AI engineer. It’s generally agreed that Python, R, Julia, Java and C++ are some of the most commonly used programming languages AI engineers will need to succeed in their role.
• Data modelling and engineering. You should feel confident with data modelling and engineering, including how to access, clean and transform data into suitable analysis formats.
• Big data analysis. You should be able to analyse and extract insights from large datasets. This requires extensive SQL and NoSQL database knowledge, as well as experience using unified analytics engines like Apache Spark.
• Machine learning and deep learning algorithms. AI engineers need to have a clear understanding of machine and deep learning algorithms, including how they function, their suitability for various tasks and practical implementation.

• Communication. You’ll need to be able to communicate complex concepts to business stakeholders who may not be as technical as you.
• Problem-solving. You’ll need to be able to come up with solutions to complex technical problems.
• Adaptability. AI is constantly changing, meaning you’ll need to be able to adapt to make the most of new tools and understand evolving concepts.
• Collaboration. You’ll need to be able to work well with other team members to come up with ideas, solve problems and deliver successful projects.
• Continuous learning. You’ll need to keep up with AI developments by putting time aside to update your knowledge and skills.

By demonstrating your suitability through your education, work experience, professional qualifications and your hard and soft skills, you position yourself as a strong candidate for a job in AI engineering.

As with all careers, there are pros and cons to AI engineering. Let’s explore them.

• High salary potential. AI engineering roles often come with higher salaries compared to other industries, especially as you progress further in your career.
• Career progression opportunities. The field offers clear paths for growth and development.
• Intellectual stimulation. Your days will be spent tackling complex challenges that require creative solutions, meaning your work will be both engaging and rewarding.
• Flexibility. Many AI engineering positions offer hybrid or remote working, allowing you to work wherever suits you best.

• High pressure. AI engineering roles are demanding, with tight deadlines and complex project requirements.
• Highly competitive job market. As a highly sought-after career path, you’ll be competing against many other applicants. That’s why it’s important to showcase your value to employers.
• Desk-based. You’ll find yourself sitting for long periods at a desk, so you’ll need to focus on movement outside of work hours.
• Automation. Some aspects of AI engineering roles are starting to be automated, requiring you to adapt and evolve.

Most AI engineers will work between 37 and 40 hours a week, typically from 9am to 5pm. However, for the sake of your personal wellbeing, you need to be mindful of work-life balance. Some AI engineers report overworking or struggling to switch off after work, which may lead to burnout if ignored. With careful management, you can prioritise wellbeing in your career and strike a good work-life balance.

Company culture plays an important role in allowing you to maintain a good work-life balance. A supportive company culture typically encourages reasonable working hours, taking annual leave and offering flexible working arrangements to make sure employees work in a way that allows them to do their best work and look after their wellbeing.

This article was published in June 2025.