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Employers need AI engineers who can work with data and machine-learning methods, then build, integrate, deploy, and maintain systems that use them. Python, data science, and machine learning recur in UK AI-expert vacancy analysis; US technology talent evidence also highlights deployment, APIs, distributed systems, and reliability. Cloud and cybersecurity support AI work, while analytical thinking, communication, and adaptability help teams use it effectively. There is no single universal checklist: the mix depends on the job’s actual work, seniority, sector, and location.
What skills appear most often in AI engineering evidence?
The recurring pattern is a stack rather than a single tool: programming and data skills, machine-learning knowledge, software and deployment skills, and the judgment and communication needed to work with others. The available figures are useful signals, not a global leaderboard: they come from different countries, studies, and role definitions.
In its 2025 analysis of UK AI expert vacancies, the Department for Science, Innovation and Technology (DSIT) reported Python in 68% of vacancies, data science in 64%, machine learning in 63%, SQL in 29%, AWS in 18%, and Azure in 11%. These are individual skill frequencies in the vacancies analyzed; skills can co-occur, so the percentages should not be added together. They describe that UK analysis, not every AI job or the global market. DSIT’s AI Skills for Life and Work summary explains the vacancy findings.
Different evidence adds capabilities that vacancy lists alone may not make visible. Indeed’s US-focused 2025 Tech Talent Report identifies model deployment, machine-learning frameworks, distributed computing and systems, RESTful APIs, site reliability engineering (SRE), and model training among in-demand or hard-to-fill technology skills. Across its G7 analysis, the AI Workforce Consortium describes cloud and software engineering and cybersecurity as foundational to AI transformation. Together, these sources support a practical conclusion: employers need people who can make models useful inside working systems, not just people who know model terminology.
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Which skills should an AI engineer build first?
Start with the capabilities that make the rest of the work possible: programming, data handling, and a sound grasp of machine learning. Add software integration and operational skills as the job calls for them. This order is a learning framework, not an employer-mandated sequence or a claim that every role uses the same tools.
Programming and data foundations
Python is prominent in the UK vacancy analysis, while SQL and data science also appear in its reported skills. An engineer needs to be able to inspect, transform, and reason about data, as well as write code that can be understood and maintained. These foundations help with the whole workflow: working with inputs, preparing data, connecting a model to an application, and diagnosing unexpected outputs.
Machine-learning knowledge matters alongside coding. It lets an engineer understand what a model is intended to do, how to assess whether it is suitable for a task, and how to interpret its behavior. The evidence identifies machine learning as a recurring requirement, but it does not establish one framework, algorithm family, or level of advanced mathematics as a universal prerequisite. Check the work described in a particular vacancy rather than assuming a framework keyword applies everywhere.
Software engineering and integration
Model and data knowledge does not by itself cover the engineering involved in connecting a model to a product or service. Indeed’s US report names APIs, model deployment, and distributed systems among the technical skills employers seek or find difficult to fill. In practical terms, these capabilities help an engineer connect components, serve model outputs to other software, and work within systems that may run across multiple machines or services.
Software development is also part of DSIT’s description of the deeper AI expert skill set. In an applied role, the balance may shift toward building features around an existing model or using AI within a domain. Either way, employers can need the ability to turn a technical component into something that fits the surrounding software. The sources do not prescribe one architecture or implementation method; the job’s systems and responsibilities determine the details.
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Deployment, testing, and reliability
Deployment is a distinct capability, not simply another name for training a model. Indeed’s report separately includes model training, model deployment, and SRE, alongside distributed computing and systems. That combination points to work on both creating models and operating software that uses them. Reliability knowledge is relevant when a system must continue to function as part of a real service.
When evaluating a role, look for clues about what “AI engineering” means in that organization: whether the job emphasizes experimentation and training, connecting models to an existing application, or maintaining an operational service. The evidence does not establish that every AI engineer owns all those tasks. It does show why a candidate’s ability to work across model and software boundaries can be valuable.
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How important are cloud and cybersecurity skills?
They are useful supporting strengths, but the evidence does not make a particular cloud platform or security credential mandatory for all AI engineers. DSIT found AWS and Azure among the skills requested in a portion of analyzed UK AI expert vacancies. The AI Workforce Consortium’s 2025 G7 report describes cloud and software engineering and cybersecurity as foundational for AI transformation initiatives.
That supports learning how cloud environments fit into the work when a target role uses them, and understanding security as part of responsible system development. It does not support treating one provider as the industry-wide standard, nor does it establish a particular certification, security control set, or cloud configuration as required for every AI job. Read the vacancy for the actual platform and responsibilities, and distinguish a preferred tool from the core engineering task.
Which AI engineering skills depend on the role?
Job titles are inconsistent, so compare responsibilities rather than relying on labels alone. DSIT distinguishes AI experts, including examples such as machine-learning engineers and AI research scientists, from AI specialists who apply AI in a domain. Its summary describes experts as needing deep understanding of advanced AI concepts alongside data science, machine learning, and software development; specialists need substantial technical acumen and data-centric skills.
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AI expert or machine-learning engineering work
Roles centered on developing or advancing AI systems are more likely to call for depth in AI concepts and machine learning, as well as the data and software foundations to implement that work. The UK government’s distinction is illustrative rather than a universal job taxonomy: employers use titles differently, and the title “machine-learning engineer” alone does not prove a consistent scope or level of expertise.
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A software developer or engineer applying AI in a field may need enough technical understanding to assess and work with AI-enabled tools, plus the data-centric skills relevant to the application. The center of gravity can be domain knowledge and integration rather than developing new AI methods. A vacancy’s listed deliverables are a better guide than the word “AI” in its title.
Specialist paths
The AI Workforce Consortium’s 2025 report discusses AI/ML Engineer, AI Risk & Governance Specialist, and NLP Engineer among rapidly growing roles across its G7 analysis. These examples show that specialization is possible; they do not mean every AI engineering position includes governance work or natural-language processing. If a role has one of these labels, use its responsibilities to identify the depth it expects in that specialty.
What human skills do employers look for alongside technical ability?
Analytical thinking and problem-solving complement technical skills, while communication and collaboration help engineers work across teams. These capabilities should not be treated as a substitute for technical depth or as a universal ranked checklist; the cited employer outlooks identify them as important alongside technology skills.
The World Economic Forum’s Future of Jobs 2025 outlook lists AI and big data, networks and cybersecurity, and technological literacy among the skills employers expect to grow in importance over the following five years. It also highlights analytical thinking, creative thinking, resilience, flexibility, agility, curiosity, and lifelong learning. This is an employer outlook, not a count of vacancy requirements. The AI Workforce Consortium also emphasizes communication, collaboration, leadership, critical thinking, and problem-solving, without providing a universal ranking for AI engineers.
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For a candidate, the practical implication is to show how technical work was reasoned through and communicated: what problem it addressed, how choices were made, and how the result fits the needs of other people or systems. Adaptability and continued learning matter in a field where tools and job requirements change, but the sources do not define a particular learning plan or credential as proof of those qualities.
How should you interpret employer skill-gap figures?
Skill gaps are a reported concern, but survey findings should not be mistaken for a census of every employer. DSIT’s 2025 AI Labour Market Survey executive summary says 97% of respondents identified at least one AI labour-market skills gap; 57% reported a technical gap and 30% a non-technical gap. These are survey results with the survey’s respondent scope, not estimates of worldwide employer prevalence. See the DSIT AI Labour Market Survey 2025 executive summary.
The results do reinforce that the gap may involve more than specialized model knowledge: respondents also identified non-technical gaps. They do not identify a single missing skill shared by all employers, and the percentages for technical and non-technical gaps should not be treated as mutually exclusive categories unless the survey itself establishes that. For applicants and teams, the useful takeaway is to define the particular work and capability gap before choosing training or hiring criteria.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can you use this evidence to assess a job or plan learning?
Because the evidence spans a UK vacancy analysis and survey, a US technology report, an international employer outlook, and a G7 consortium report, it cannot be combined into one ranking. The most reliable way to apply it is to match skills to the role’s tasks and geography, then separate foundational needs from optional specialization.
- Identify the work. Note whether the vacancy focuses on developing AI methods, preparing data, applying a model in a domain, integrating it into software, or operating a service. Do not infer all of these responsibilities from the title.
- Check the foundations. Look for programming, data handling, and machine-learning expectations. Python, data science, machine learning, and SQL recur in the UK analysis, but that does not make every item mandatory for every employer.
- Look for system responsibilities. If the role mentions deployment, APIs, distributed systems, or reliability, assess whether you can work on those parts of a software service as well as understand its model component.
- Separate platform mentions from broad capability. Cloud or security may matter to the organization’s environment. Confirm which provider or responsibility is actually named instead of assuming a particular platform or certification is required across the market.
- Match the expected depth to the level. A role developing AI may call for deeper AI concepts; a role applying AI in a domain may emphasize technical acumen and data-centered work. Verify the distinction against the listed duties and seniority.
- Consider how work is done with others. Where collaboration, communication, judgment, or learning is relevant, be ready to explain decisions and trade-offs, not just list tools.
Use this as a way to read a vacancy, not as a checklist that every applicant must complete. The cited evidence does not provide a comparable breakdown by seniority and specialization across markets, and the studies use different definitions and time periods. Requirements can change, so precise claims about the latest rankings should be checked against the relevant location and current job postings.
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What does the evidence establish—and what does it not?
The strongest conclusion is that employers need combinations of AI, data, and engineering capabilities, supported by cloud or cybersecurity knowledge where relevant and complemented by analytical and interpersonal skills. The sources establish recurring clusters and role distinctions, not a universal job specification.
Geography matters: the detailed skill percentages above come from UK AI expert vacancy analysis; Indeed’s technology report is US-focused; the Consortium’s analysis covers G7 countries; and the WEF material is an outlook on employer expectations. Their methods and time frames differ, so their findings should be read alongside one another, not collapsed into a global ranking. None of the cited material establishes a single required framework, security certification, degree, or level of mathematical specialization for all AI engineers.
Frequently Asked Questions
Do AI engineers need a computer science degree?
The cited vacancy analysis, reports, and employer outlook do not establish a universal degree requirement. Check the education and experience criteria in the specific vacancy rather than treating a degree as either mandatory or irrelevant across the field.
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The evidence supports machine-learning knowledge and, for AI expert work, deep understanding of advanced AI concepts, but it does not specify a universal mathematics threshold. A role focused on applying AI in a domain may have a different technical depth from one developing AI methods.
Is a cloud certification necessary to become an AI engineer?
The sources identify cloud skills as useful and foundational in some AI transformation contexts, but they do not name a required certification. A certification should not be assumed to substitute for the cloud experience or responsibilities a particular employer specifies.
Are AI engineering skills the same in the UK and US?
The available evidence is not a like-for-like comparison: the UK figures concern AI expert vacancies, while the US report addresses technology talent more broadly. It therefore cannot establish a direct country-by-country ranking or show that every skill is valued equally in both markets.
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