A company adding AI to its customer support product and a company developing a new machine-learning model may both advertise for an “AI engineer.” The work, and the person they need, can be quite different.
Emerging technologies create demand for people who can research, build, integrate and operate new systems. For employers in Europe, the hiring challenge is identifying which of those capabilities a project actually needs. A technology’s popularity tells you little about the responsibilities of your next hire.
Start with the problem the team needs to solve. Then decide which skills are missing, whether someone internally can develop them, and what a new colleague would take responsibility for.
What counts as an emerging technology?
An emerging technology is still developing in capability, adoption or practical use. It may be new, or it may be an established field finding new applications. AI, connected industrial systems, extended reality and quantum technologies can each fit that description, although their readiness varies by application.
Cybersecurity, cloud engineering and data engineering are established disciplines that support many of these projects. They belong in the hiring conversation because a new application still needs reliable data, secure access and systems that someone can maintain.
What the European figures tell us about skills demand
In 2025, around 20% of EU enterprises with at least 10 people employed used AI technologies, up from about 13.5% in 2024, according to Eurostat’s enterprise survey.
The same survey found that 70.9% of enterprises that had considered AI but were not using it identified a lack of relevant expertise as a reason. That percentage refers to a specific group of businesses considering adoption; it does not describe all European employers.
These figures measure adoption and reported barriers. Hiring demand still depends on the role, sector and company. A manufacturer connecting production equipment and a software business adding AI features will need different combinations of skills, even when both describe their plans as digital transformation.
Which roles support emerging technology projects?
Use the work below as a starting point for a role brief. Responsibilities may be shared across an existing team, a new hire and specialist external support.
| Technology or field | Work the project may involve | Relevant roles to consider |
|---|---|---|
| AI and machine learning | Model development, application integration, evaluation and production monitoring | ML engineer, applied AI engineer, data engineer, MLOps engineer |
| Cybersecurity | Secure system design, threat detection, incident response and security testing | Security architect, security engineer, incident responder, penetration tester |
| Industrial IoT, robotics and autonomous systems | Connecting devices, processing sensor data, developing controls and testing physical behaviour | Embedded software engineer, robotics engineer, controls engineer, systems test engineer |
| Energy and climate technology | Grid integration, equipment control, operational data analysis and energy software | Power systems engineer, electrical engineer, controls engineer, data or software engineer |
| Augmented and virtual reality | Building interactive environments, rendering graphics and integrating hardware | Real-time 3D developer, graphics engineer, XR developer |
| Blockchain applications | Developing distributed applications, smart contracts and supporting infrastructure | Blockchain developer, backend engineer, security specialist with relevant protocol experience |
| Quantum technologies | Research, hardware development, control systems and specialist software | Quantum researcher, experimental physicist, electronics engineer, quantum software specialist |
The combination matters. A connected device project may need strong embedded engineering before it needs advanced analytics. An AI feature may depend on data access and software integration before custom model development becomes useful.
Separate AI research, product development and operations
An AI role becomes easier to define once the hiring team can describe what already exists.
Is the company training a model? Adapting an existing one? Integrating a model into a product? Taking an unreliable prototype into production?
For an illustrative example, consider a business building an internal assistant that answers questions from company documents. If it uses an existing model, the immediate work might involve document retrieval, access permissions, evaluation, application integration and monitoring. The role brief should explain those responsibilities and the support available from the wider team.
A project developing a new model may need deeper experience in experimentation, training data and model evaluation. A production system also needs deployment, testing and monitoring, which Google Cloud’s MLOps guidance treats as part of the engineering lifecycle.
Our comparison of AI engineers, ML engineers and MLOps engineers can help you separate those responsibilities. Job titles overlap between companies, so write down who owns each part of the work.
Give cybersecurity roles a defined remit
“Improve our security” is too broad to assess a candidate against.
A company investigating recurring incidents needs different experience from one redesigning cloud access controls or preparing a secure product architecture. Decide which problem takes priority and what support the person will have.
ENISA’s European Cybersecurity Skills Framework describes 12 professional role profiles, with associated responsibilities, skills and knowledge. It provides a useful reference when a vacancy has accumulated tasks from several security disciplines.
If the brief includes incident response, architecture, penetration testing and governance, check whether the workload and available team support make that scope realistic for one person.
Account for the equipment and working environment
Hiring for robotics, connected devices and energy systems requires a clear description of where the engineering happens.
Will the person need regular access to a laboratory, factory, vehicle or test installation? Who handles commissioning and maintenance? How much travel is involved? These details affect the search location and should appear in the brief before candidates enter interviews.
The IEA’s World Energy Employment 2025 report, which examines the global energy workforce, identifies shortages in applied technical occupations, including engineers and skilled trades. Energy technology hiring can involve electrical, controls and field expertise alongside software development.
For a European search, separate the work that can be done remotely from tasks requiring physical access. Then define the office, site and travel expectations precisely.
Match specialist hiring to the project’s stage
A research programme, a pilot and a service used by paying customers have different staffing needs.
Before opening a specialist role, agree what the project needs to achieve, how it is funded and what happens after the first demonstration. This is particularly useful for projects involving quantum technologies, blockchain or extended reality, where an exciting proposal can still lack a clear operating plan.
The European Commission’s Quantum Europe Strategy, adopted in July 2025, includes skills among its priorities. Quantum work spans computing, communication and sensing. That range makes the application and technical environment essential parts of a vacancy; a general claim that quantum jobs are growing cannot define the expertise required.
Apply the same discipline elsewhere. For an XR project, specify the users, hardware and intended experience. For a blockchain project, explain why the proposed architecture fits the problem and who will review its security. Candidates need enough detail to judge whether their experience is relevant.
What to put in the hiring brief
Before sourcing candidates, the recruiter and hiring manager should agree on five things:
- The first useful outcome. Describe what the person should help deliver: a tested prototype, a production integration, a security improvement or a working hardware component. Include dependencies they will need help with.
- The expertise required on arrival. Separate essential experience from tools or domain knowledge that can be learned. Explain why each requirement matters to the work.
- The existing team’s capabilities. Record who can mentor, review work and own related systems. A specialist joining an established team has a different job from someone building the function alone.
- The working arrangement and package. Confirm the employing location, remote or site requirements, travel, salary range and relevant package components before outreach begins.
- The evidence needed to make a decision. Agree what interviews and practical discussions must establish, who assesses each area and who makes the final decision.
For compensation planning, use the European tech salary calculator as an indicative reference alongside current salary sources and candidate conversations. Compare equivalent responsibilities, seniority, locations and package components.
Leave room to revise the brief when conversations reveal a mismatch. If relevant candidates consistently interpret the role differently, revisit the responsibilities and title together.
Decide what the existing team can learn
Some projects require expertise the business does not yet have. Others need experienced employees to learn a new tool or apply familiar skills in a different setting.
Review the gap before opening a vacancy. Can a backend engineer take on an integration with specialist support? Can a data engineer develop the required platform skills? Is there someone qualified to supervise the work while they learn?
Training needs protected time, a practical project and access to someone who can review progress. For work carrying significant technical or safety responsibility, establish how competence will be assessed before assigning independent ownership.
Our guide to employee upskilling in European companies explores how to organise that learning. Hiring and development can happen together: a new specialist may lead the work while helping colleagues build the skills to maintain it.
Assess the work candidates will actually do
Choose an assessment that reflects the decisions the person will face.
An applied AI candidate might discuss how they would evaluate a document assistant and investigate incorrect answers. An embedded engineer might explain a debugging approach for a provided device scenario. A security candidate might work through a fictional incident and explain their priorities.
Keep the scope manageable, share the expected time commitment and use consistent evaluation criteria. Ask candidates to explain assumptions and trade-offs. A polished answer is less useful if the team cannot understand the reasoning behind it.
For more detail, see our guide to assessing engineering skills through practical work.
Frequently Asked Questions
Prioritise technologies connected to a defined business or research need. AI, connected systems, energy technology and cybersecurity may be relevant to very different projects. Start with the intended outcome, available data or equipment, budget and existing team before deciding which expertise to hire.
The staffing requirement depends on what the company is building. Using a standard AI product, integrating an existing model and developing a new model involve different work. Define the responsibilities first, then decide whether they belong with the existing team, a new hire or specialist support.
Yes, where their existing skills provide a suitable foundation and the learning gap is manageable. Software integration, data engineering and systems experience may transfer into related work. Research, hardware and other specialist responsibilities can require substantial additional training. Assess the gap and provide supervision appropriate to the work.
Set requirements around the evidence the role needs. Research-intensive positions may depend on advanced academic training. Other engineering roles can be assessed through relevant projects, practical experience and technical discussion. Explain any qualification requirement and check whether it is necessary for the responsibilities involved.
A recruitment partner can help clarify the role, identify relevant candidate backgrounds, gather feedback on the brief and coordinate the search. Agree how technical competence will be assessed and which decisions remain with the hiring team. The value should be visible in relevant conversations and a search that reflects the work.
Turn your technology plans into a clear hiring brief
If you know what you want to build but are unsure which specialist to hire, tell us about the project. Tech StaQ can work through the responsibilities, essential skills and existing team support with you, so the search starts with a role candidates can understand.