If you’re already technically-minded and work regularly with Python and data analysis tools, a career as an Artificial Intelligence (AI) Engineer could be for you. AI is booming. As more organisations experiment with the technology for competitive gain, demand for AI Engineers will only continue to increase.

Are AI engineers in high demand?

Being an AI Engineer means you are in the best position to solve complex problems. You’ll get hands-on with cutting-edge technology, and master the new skills needed to develop more technically advanced products. 

Besides having an immensely satisfying job, you’ll also make yourself marketable when you want to take the next step in your career. AI skills are in huge demand – and you’ll possess them as well as engineering experience, which makes you a more attractive prospect.

According to McKinsey, there are two key roles rising in prominence due to AI: the engineer and product manager. Today, AI Engineers are the fourth most difficult role to hire for. Furthermore, the ‘AI Engineers Demand Outlook’ report indicates that demand is expected to grow at a CAGR of 20.17%, to reach a market size of $9.5M in 2029 (up from $3.8M today). Compare that to software  engineering, which is growing at a CAGR of 5.9%, and establishing a career in AI is a good choice to acquire in-demand skills that future proof your career. 

What do AI Engineers do?

As an AI Engineer, your role is to productise AI. By using AI and Machine Learning (ML) techniques to develop tools, frameworks and processes for real-world applications, you help organisations to solve complex problems, optimise processes, and make better informed decisions. For example, AI products could include speed up medical diagnosis and treatment, chatbots to enhance customer service in retail, algorithms that spot potentially fraudulent activity on financial accounts, or tools that speed patient diagnosis.

If you’re keen to develop your career so you sit at the forefront of the AI revolution, upskilling as an AI Engineer is a smart choice.

AI Engineer responsibilities

The role of AI Engineer extends beyond the technical domain to encompass softer skills. This is because you will have the important role of bridging the gap between how AI theoretically benefits a business, and the real-world applications your AI products are built for. This means you need the skills to understand user requirements, communicate effectively with stakeholders, and get buy-in from your organisation.

Your responsibilities as an AI Engineer will therefore include:

• Developing and optimising AI models using ML algorithms, deep learning neural networks and large language models (LLMs).
• Ethical and responsible AI development to ensure potential biases are accounted for and mitigated, so they cannot perpetuate further.
• Continuous learning – AI is a fast-paced domain with new developments being made every day. You must find ways to keep your skills up-to-date to stay relevant.
• Product management to ensure the right initiatives are pursued, so products are ultimately useful, usable, and used.
• Change management and communication to help your organisation understand and accept AI models, so they are trusted and adopted into business as usual. 

Is AI engineering a good career for the future?

If you’re looking for a rewarding, fast-paced career where you have the skills to help organisations solve their most complex problems, AI engineering is definitely worth a look. In the following sections we explore what you can expect from specialising in the domain.

AI Engineer salary expectations

As an AI Engineer, you can command a higher salary. According to industry data, ML/AI Software Engineers can expect a median salary of £104k, compared to £85k for a general Software Engineer. Furthermore, a quick search of LinkedIn reveals this can rise to £150k with five years’ experience.

AI Engineer skills

At Cambridge Spark we set the gold standard in data, analytics and AI training, because we’re always first to market with recognised certifications and qualifications. We asked our expert faculty of industry practitioners and academics what they believe the core skills of an AI Engineer are. Read the blog here...

AI Engineer roadmap

As your career progresses, you could expect your path to take one of two routes:

Specialise further in the technology: through an AI and Data Science Apprenticeship (L7) you could refine your skills further with elective specialist pathways into MLOps, DataOps, or advanced data science. Alternatively, you could opt for dedicated courses on subjects like computer vision and robotics. Or explore adjacent careers in AI technical product management or AI ethics.

Specialise further in a sector: AI Engineers are becoming commonplace outside the IT and tech sector, which opens opportunities for you to specialise in industries and domains, such as healthcare, finance, manufacturing, and more.

“After my computer science degree dipped into data mining, I had a glimpse of how large-of-an-area data science is. By using the skills you learn at work, you are able to get a much better picture than you could from a typical Master’s degree - where the biggest limitation is the lack of importance placed on production and cloud engineering.”

Nicholas Orford-Williams, L7 AI Data Specialist Apprentice, BBC

How to become an AI Engineer

McKinsey has said that current approaches to talent management are not effective to develop AI skills in this new age. Instead, they advocate nurturing talent through apprenticeship models. It says, “Apprenticing offers hands-on learning to demystify change and role modelling to demonstrate hard-to-teach skills, such as problem-solving mindsets and how to use good judgment in evaluating code suitability.”

Therefore, the best way to acquire the skills you need to become an AI Engineer is to invest in an apprenticeship programme, where you’ll receive theoretical knowledge, hands-on experience, and safe spaces to test your new skills.

With Cambridge Spark's AI Engineer Apprenticeship (L6) you now have a new path to AI expertise. It can be funded via the apprenticeship levy, and because it is assessed against the Level 6 Machine Learning Engineer Standard, you receive a formal, recognised AI qualification once you complete the programme.

To learn more about what it’s like to study with Cambridge Spark, and how the AI Engineer Apprenticeship aligns with your current work commitments, please visit our dedicated page: