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Finding the right AI talent is becoming a more complex challenge for UK recruitment agencies.
However, the issue is not simply that there are fewer candidates available. The real challenge of recruitment agencies like yours are identifying professionals who combine specialist AI skills with practical experience of building, deploying and improving AI solutions.
A title such as AI Engineer or Machine Learning Engineer can mean different things depending on what your client actually needs.
In this blog, you will explore which AI roles create more complex searches, why experienced candidates are difficult to find and how you can improve your approach to sourcing and screening specialist talent.
Which AI roles are most difficult to fill in the UK?
Some AI roles often create more challenging searches because they require a combination of specialised skills, practical experience and the ability to apply AI solutions in real business environments.
For example, your client may ask for an AI Engineer, but they may need someone who understands machine learning, software development, cloud environments and production deployment.
This is why your recruiters need to look beyond job titles and understand the capability behind each role. Let us explore the key AI roles that often require a more focused sourcing approach.
Machine Learning Engineers
The usual expertise of a machine learning engineer is to develop and improve AI models using data, algorithms and programming skills.
However, these days, many clients need candidates who have more than theoretical knowledge. They want candidates who can apply machine learning in real business environments and demonstrate how their work supports measurable outcomes.
Hence, when you assess these candidates, your team should look beyond keywords. Ask your team to check whether they have built AI models, worked with Python, used relevant frameworks, and supported solutions after deployment.
Applied AI and LLM Engineers
This is another profile that is often counted as the most challenging searches in the UK.
Applied AI roles have grown as businesses move from AI experimentation into practical use cases.
These professionals often build AI applications, connect large language models with business systems, and improve how organisations use AI in practical situations.
So, when your team sources these candidates, your recruiters need to understand whether “applied AI and LLM engineers” have used AI tools or delivered a production-ready solution.
MLOps and AI Platform Engineers
MLOps professionals help organisations deploy, manage, and monitor AI systems. Their work often combines software engineering, cloud infrastructure, and automation.
Your client may require experience with cloud platforms, deployment pipelines, monitoring tools, and scalable AI environments.
Because these skills sit across multiple technical areas, finding the right profile can take more focused sourcing.
AI Data Engineers
AI Data Engineers play an important role in building the data foundations required for successful AI solutions.
They build data pipelines, manage data quality, and create infrastructure that allows AI systems to perform effectively.
When your team recruits these roles, they need to understand how candidates support AI delivery through data architecture, engineering practices, and reliable data environments.
The table below will help your team understand which skills, experience and capabilities to verify for each AI role.
| Role | Skills to verify |
|---|---|
| Machine Learning Engineer | Machine learning, Python, model development and deployment |
| Applied AI Engineer | LLMs, APIs, AI applications and evaluation |
| MLOps Engineer | Cloud, automation, infrastructure and monitoring |
| AI Data Engineer | Data pipelines, platforms and data quality |
The UK Government’s AI Labour Market Survey found that 35% of organisations struggled to fill AI roles, with technical skills and experience gaps creating additional recruitment challenges.
Understanding the roles is the first step. Next, you need to understand why these candidates are difficult to find.
Why are AI candidates difficult to find?
AI candidates are difficult to find because experienced professionals with combination of technical skills and proven commercial delivery experience are high in demand.
Many candidates may have exposure to AI technologies, but fewer have worked on real-world projects where they have built, deployed or managed AI solutions at scale.
The challenge becomes even greater when job briefs include multiple requirements, such as
- Specific AI skills
- Industry experience
- Cloud expertise and
- Location preferences
While each requirement may be relevant, combining them can significantly reduce the available talent pool.
Hence, your recruitment team should focus on understanding which requirements are essential and where flexibility exists.
For your recruitment team, the best way is to give your team a well-defined brief about the job (candidate profile or requirement) as it helps your team to identify suitable candidates faster and build stronger shortlists.
Once you understand the market challenge, the next step is to know how to assess AI candidates effectively.
What should recruiters look for beyond keywords?
Your recruiters should look for evidence behind the experience and assess whether candidates have the right AI skills for the role.
Your recruiters should look beyond the technology names and understand the evidence behind the candidate’s experience.
This thought process will help your team to identify whether they have the right AI skills for the role and whether they can apply those skills in real working environments.
To assess, your team should ask the following questions:
- What did the candidate build?
- What part of the project did they own?
- Did the solution reach production?
- Which technologies did they use?
- What outcome did their work achieve?
For example, an Applied AI Engineer should be able to explain how they developed and improved an AI application. An MLOps Engineer should understand how AI systems are deployed, monitored, and managed.
Moreover, before starting a search, agree with your client about which skills are essentially preferred or trainable.
This can help your recruiters focus on what genuinely matters instead of rejecting suitable candidates because they do not match every requirement.
With the right qualification process, you can build stronger shortlists and improve your AI recruitment outcomes.
How can agencies improve AI candidate sourcing?
Agencies can improve AI candidate sourcing by looking beyond job titles and focusing on skills, responsibilities and transferable experience.
A successful AI recruitment approach requires your recruiters to identify candidates with relevant AI skills, even when their previous job title does not include “AI”. For example, software engineers, data engineers, and cloud specialists may have valuable experience for AI roles.
Your team should:
- Map related job titles and skill areas.
- Review existing candidate databases.
- Identify transferable technical skills.
- Engage passive candidates.
A structured candidate sourcing strategic process helps your recruiters build stronger talent pools and deliver better shortlists for specialist technical recruitment searches.
If specialist technology searches are creating delivery pressure for your team, IMS can help extend your recruitment capability through sourcing support, screening support, and market mapping.
How IMS supports UK recruitment agencies with AI talent delivery
IMS helps UK recruitment agencies improve their AI recruitment delivery through market mapping, candidate sourcing, screening support, and database management.
By combining People + AI, IMS brings together experienced recruitment professionals with AI-enabled workflows to improve sourcing efficiency, screening consistency and candidate matching.
Technology can help your team organise information, improve processes, and work more efficiently. However, human judgement remains essential. Your recruiters and clients should continue to make decisions around candidate suitability, technical capability and final placements.
With the right combination of people, processes, and technology, agencies can build a stronger delivery model for specialist technical recruitment.
Conclusion
Successful AI recruitment is not about finding candidates with the right keywords on a CV. It is about understanding the capability behind the title, identifying the skills that matter and verifying experience that delivers real outcomes.
For recruitment agencies, a stronger approach to sourcing, screening, and market mapping can help you handle specialist AI searches with greater confidence.
Frequently Asked Questions
Does every AI role require a PhD?
No, every AI role does not require a PhD. While research-focused AI roles may need advanced academic qualifications, many AI engineering roles depend on practical experience, software skills and production delivery. In AI recruitment, you should focus on the AI skills needed for the role rather than relying only on qualifications.
How can recruiters assess AI candidates without deep technical expertise?
Recruiters can assess AI candidates by reviewing evidence of their experience, projects, and outcomes. During technical recruitment, focus on what they built, the tools they used and their responsibilities. A structured candidate assessment process helps identify relevant skills before technical teams complete deeper validation.
How should agencies handle unclear AI job titles?
Agencies should look beyond AI job titles and assess candidates based on their skills, responsibilities, and experience. Titles such as AI Engineer or Machine Learning Engineer can vary between organisations. Strong candidate sourcing focuses on demonstrated capability, technical experience, and proven results.
