AI is changing software development, but that does not mean every Software Engineer needs to become an AI specialist.
For employers, the more useful question is how a candidate uses AI within their existing engineering work. Can they use AI tools to improve productivity? Can they check the output? Do they understand the risks? Can they recognise when an AI-generated solution is wrong?
These questions are becoming more important as AI becomes part of everyday software development. Hiring teams need to assess both traditional engineering ability and the new skills that allow developers to work effectively with AI.
What AI Skills Should Software Engineers Have?
The right AI skills depend on the role.
A Software Engineer might use AI to generate code, explain unfamiliar systems, write tests, identify bugs or speed up routine development work. A senior engineer may also need to review AI-generated solutions, assess their risks and decide where AI should be used within the development process.
Skills England’s 2026 research shows that AI is changing software development, with greater emphasis on verification, oversight, judgement and communication as AI takes on more routine development tasks.
For employers, this means that AI experience should not become another keyword to add to a job description. The important question is what the candidate can actually do with AI and how well they understand its limitations.
Look at How Candidates Use AI
Asking whether a candidate has used ChatGPT, GitHub Copilot or another AI development tool will tell you very little on its own.
Instead, ask how they use those tools.
A strong candidate should be able to explain when AI helps them, when they avoid using it and how they check its output. They should also understand that an AI tool can produce an answer that looks convincing without being technically correct.
This distinction matters because effective AI use requires more than knowing which button to press. Software Engineers still need to understand the problem, assess the proposed solution and remain responsible for the result.
Assess AI-Assisted Code Review
One of the most useful ways to assess AI skills is to look at how candidates review generated code.
AI can produce functional-looking code that contains bugs, security weaknesses, inefficient logic or design problems. An engineer therefore needs enough technical understanding to question the output rather than accept it automatically.
A practical recruitment exercise could give candidates a piece of code and ask them to identify potential problems, explain their reasoning and suggest improvements. The code could come from an AI tool, but it does not have to. The objective is to assess engineering judgement.
This approach also avoids turning the interview into a test of whether someone knows a particular AI product.
Don’t Confuse AI Tool Experience With AI Expertise
A candidate who has used AI extensively is not automatically more capable than one who has only recently started using it.
Tool experience can be useful, but employers should distinguish between familiarity and capability.
The UK Government’s 2026 AI Labour Market Survey identifies significant skills gaps and evolving skills requirements within the AI sector, highlighting the growing challenge of finding people with the capabilities organisations need.
For Software Engineer recruitment, this means employers should define the level of AI capability they actually need rather than assuming that more tool experience always means a better candidate.
Test Software Engineering Without AI
AI skills should add to strong engineering fundamentals, not replace them.
Candidates still need to understand software design, debugging, testing, architecture and the principles behind the systems they build. They should also be able to reason through a technical problem when an AI tool cannot provide a useful answer.
This is particularly important when hiring senior engineers. Someone who relies heavily on AI but cannot explain the underlying solution may struggle when a system behaves unexpectedly or when the generated answer does not fit the organisation’s architecture.
Technical assessments should therefore test understanding as well as output.
Assess Security and Responsible AI Use
Software Engineers may work with source code, customer information, credentials, intellectual property and other sensitive data. Using AI tools can introduce additional risks if employees do not understand how those tools handle information.
Responsible AI use should therefore form part of the assessment for relevant roles.
The UK’s 2026 AI Skills for Life and Work employer research identifies different levels of AI capability across organisations, including people who use AI tools, implement AI models and develop AI models.
For employers, this reinforces the importance of defining what AI responsibility looks like within the specific role.
An interview could explore how a candidate would decide whether to put a piece of proprietary code into an AI tool, how they would validate an AI-generated answer or what they would do if an AI-assisted solution introduced a security concern.
Senior Engineers Need More Than Tool Knowledge
The level of AI capability required should increase with the level of responsibility.
A junior Software Engineer may need to understand how to use approved AI tools safely and check the work they produce. A Senior Software Engineer may need to make decisions about where AI fits into development workflows and how teams should review AI-assisted work.
Engineering Leads and senior technical specialists may need to consider wider questions around architecture, security, governance, quality and accountability.
That means employers should avoid using one generic definition of “AI skills” across every Software Engineering vacancy.
Use AI in the Assessment Process Carefully
AI has also changed the way employers assess candidates.
Traditional coding tests can become less useful if candidates can simply ask an AI tool to produce a solution. However, banning AI entirely may create an artificial assessment that does not reflect the working environment.
A better approach is to decide what the assessment is intended to measure.
If the goal is to assess independent technical reasoning, give candidates an opportunity to demonstrate that without AI assistance. If the goal is to understand how they work with AI, allow appropriate tools and assess how they verify and improve the results.
The assessment should reflect the actual expectations of the role.
Look for Learning and Adaptability
AI tools will continue to change. A candidate who knows one particular coding assistant today may need to work with a completely different tool in the future.
Employers should therefore look for people who can learn, experiment and adapt rather than focusing too heavily on a fixed list of AI products.
2026 research from the UK Government and Skills England highlights the need for employers to build workforce capability so that people can use AI effectively, safely and responsibly.
For Software Engineer recruitment, that makes learning ability particularly valuable. The strongest candidate may not have used every tool an organisation currently uses, but they should be able to understand new technology and work out how to use it responsibly.
Don’t Add AI Requirements Just for the Sake of It
There is a risk that employers will start adding “AI experience” to every Software Engineer vacancy simply because AI is becoming important.
That can unnecessarily reduce the candidate pool.
A Software Engineer developing machine-learning systems will need very different AI expertise from a full-stack developer who uses AI to support coding, testing and documentation. Treating both roles as requiring the same AI background makes the recruitment process less accurate.
Before advertising a role, hiring teams should establish what AI will actually mean for the person’s day-to-day work.
That makes it easier to distinguish genuine requirements from desirable experience.
What Should Employers Look for When Hiring Software Engineers?
AI is changing the skills employers need from Software Engineers, but the fundamentals of good engineering remain.
The strongest candidates will combine technical knowledge with problem-solving, judgement and the ability to work effectively with new tools. They should understand what AI can do, where it can create problems and when human review remains essential.
For hiring teams, the goal is not to find the candidate who uses AI the most. It is to find someone who can use AI productively without losing the engineering judgement needed to build reliable, secure and maintainable software.
As AI becomes a normal part of software development, that ability to combine technical expertise with responsible AI use is likely to become an increasingly important part of Software Engineer recruitment.
