The Most In-Demand AI Roles in 2027

Artificial intelligence is changing the way businesses build products, serve customers and operate their teams. But as more companies move from experimenting with AI to putting it into everyday use, one challenge keeps coming up: finding the people who can actually make it happen.

The demand for AI talent is growing, but the roles companies need are changing too. Alongside established positions such as Machine Learning Engineers and Data Scientists, employers are looking for specialists who can build AI-powered products, deploy autonomous agents, manage AI infrastructure and make sure these systems are secure and reliable.

So, which AI roles are likely to be most in demand in 2027? Based on current hiring trends and industry forecasts, here are seven roles businesses should have on their radar.

Why AI hiring is changing in 2027

The AI job market is moving beyond research and experimentation. Companies are increasingly focused on applying AI to real business problems, integrating it into existing products and making it work reliably at scale.

This shift is already visible in hiring data. According to PwC’s 2026 AI Jobs Barometer, UK job postings for specialist AI roles increased by 61% in 2025. Demand for roles that apply AI skills within existing professions grew even faster than demand for specialist AI developers.

The skills companies are looking for are changing as well. Michael Page’s 2027 Technology & Transformation Salary Guide highlights Applied AI Architects and Engineers among the most in-demand technology roles for 2027.

In other words, businesses don’t just need people who understand AI. They need people who can build it, integrate it, maintain it and turn it into something useful.

Seven AI roles likely to be in high demand in 2027

1. Applied AI Engineer

Applied AI Engineers are focused on putting AI into practice. Rather than developing models purely for research, they build applications that use existing models and AI technologies to solve specific business problems.

They might develop an AI-powered customer support tool, automate internal workflows, build intelligent search functionality or integrate large language models (LLMs) into an existing product.

What companies look for:

• Strong Python and software engineering skills.
• Experience working with LLM APIs and AI frameworks.
• Knowledge of RAG (retrieval-augmented generation) and vector databases.
• Experience deploying AI applications into production.
• An understanding of how to evaluate and improve AI outputs.

Why demand is growing: More businesses have moved beyond testing AI tools and are now looking for engineers who can turn successful experiments into working products. Applied AI skills are therefore becoming increasingly valuable across SaaS, fintech, healthcare and enterprise technology.

2. Machine Learning Engineer

Machine Learning Engineers are responsible for building, training and deploying systems that learn from data. They work between software engineering, data science and AI research, making sure machine learning models can operate reliably in real-world environments.

Typical projects include recommendation engines, fraud detection systems, predictive analytics, computer vision and personalised product experiences.

What companies look for:

• Strong Python and machine learning fundamentals.
• Experience with frameworks such as PyTorch or TensorFlow.
• Knowledge of model training, evaluation and optimisation.
• Experience working with large datasets.
• Familiarity with cloud platforms and production environments.

Why demand is growing: Machine Learning Engineers remain essential for businesses developing their own AI capabilities. While many companies use ready-made models, others need specialists who can customise, optimise and build models around their data and products.

3. AI Infrastructure Engineer / MLOps Engineer

Building an AI model is one thing. Making sure it runs reliably, efficiently and at scale is another.

AI Infrastructure and MLOps Engineers build and maintain the systems that support AI development and deployment. They manage the infrastructure, automation and monitoring needed to move models from development into production.

What companies look for:

• Experience with cloud platforms such as AWS, Azure or Google Cloud.
• Knowledge of Docker, Kubernetes and CI/CD.
• Experience with model deployment and monitoring.
• Understanding of data pipelines and infrastructure automation.
• Familiarity with model performance, scalability and cost optimisation.

Why demand is growing: As companies deploy more AI applications, infrastructure becomes a bigger part of the challenge. Businesses need specialists who can keep systems reliable, manage computing costs and make it easier for engineering teams to build and release AI products.

4. Agentic AI Engineer

AI agents are becoming an increasingly important part of the conversation. Unlike traditional AI applications that respond to individual prompts, AI agents can carry out multi-step tasks, use tools, interact with other systems and make decisions within defined boundaries.

Agentic AI Engineers build these systems and connect them to the tools and workflows businesses already use.

What companies look for:

• Strong software engineering and Python skills.
• Experience building LLM-powered applications.
• Knowledge of agent frameworks and tool integrations.
• Understanding of APIs, orchestration and multi-agent systems.
• Experience with testing, evaluation and monitoring.

Why demand is growing: Businesses are exploring how AI agents can automate complex workflows, from customer operations to software development and data processing. This creates demand for engineers who understand both the underlying technology and how to make agents useful in a business environment.

The role is also closely connected to the rise of Forward Deployed Engineers, who work directly with customers to implement and adapt complex technology in real-world environments.

5. AI Solutions Architect

AI Solutions Architects design how AI systems fit into a company’s wider technology environment. They connect business requirements with technical solutions, deciding which models, platforms and infrastructure are needed to deliver a particular use case.

They often work across engineering, product, data and business teams, helping organisations move from an idea or proof of concept to a scalable solution.

What companies look for:

• Strong understanding of AI and machine learning technologies.
• Experience with cloud architecture and system integration.
• Knowledge of APIs, data platforms and security.
• Ability to assess different AI models and platforms.
• Strong communication and stakeholder management skills.

Why demand is growing: Many organisations know they want to use AI but aren’t sure how to implement it across their existing systems. They need people who can make technical decisions, manage complexity and connect AI projects to wider business goals.

6. AI Product Manager

AI Product Managers sit between technology, business and users. Their job is to identify where AI can improve a product, define what should be built and work with engineering and data teams to deliver it.

Unlike traditional product management, the role often involves understanding model capabilities, data requirements, AI limitations and how to measure the quality of AI-generated results.

What companies look for:

• Product management experience, ideally in SaaS or technology.
• Understanding of AI and machine learning concepts.
• Experience defining product roadmaps and requirements.
• Ability to work closely with engineering and data teams.
• Strong commercial awareness and user focus.

Why demand is growing: As AI becomes part of more products, companies need product leaders who can identify valuable use cases and make sure AI features solve actual customer problems. Technical knowledge matters, but so does the ability to understand what users need and what the business is trying to achieve.

7. AI Security Engineer

AI systems introduce security challenges that go beyond traditional software vulnerabilities. They can expose sensitive data, be manipulated through malicious inputs or produce outputs that create risks for businesses and users.

AI Security Engineers focus on identifying and reducing these risks. They work on securing AI models, applications, data pipelines and the infrastructure around them.

What companies look for:

• A background in cybersecurity or software security.
• Understanding of LLMs and AI application architecture.
• Knowledge of data protection and access controls.
• Experience with vulnerability testing and threat modelling.
• Familiarity with AI-specific risks, such as prompt injection and data leakage.

Why demand is growing: As AI becomes more deeply integrated into business operations, security and governance become harder to ignore. Companies need specialists who understand both traditional cybersecurity and the risks introduced by AI-powered systems.

What skills will make AI candidates stand out in 2027?

Job titles can be misleading in the AI market. Two companies might advertise an AI Engineer role but expect completely different things from their candidates.

One might need someone with strong machine learning fundamentals who can train and optimise models. Another might be looking for a software engineer experienced in integrating LLMs into customer-facing products.

That is why employers should look beyond job titles and focus on the skills needed for the actual project.

Some of the capabilities likely to remain particularly valuable in 2027 include:

• Applied AI experience: the ability to take AI from a prototype to a production environment.
• Strong software engineering fundamentals: Python, system design, APIs and scalable architecture.
• Data expertise: understanding how to prepare, manage and work with data effectively.
• Cloud and infrastructure knowledge: experience deploying and maintaining AI systems.
• AI evaluation and security: knowing how to test outputs, manage risks and build reliable applications.
• Commercial awareness: understanding the problem the technology is meant to solve.

Technical skills are only part of the picture. Communication, adaptability and the ability to work across different teams are also important, particularly for roles that connect AI development with business needs.

What makes AI talent difficult to hire?

The challenge isn’t simply that AI is a popular field. It’s that the technology is evolving faster than many companies can update their hiring requirements.

There are several reasons why finding the right candidates can be difficult:

1. Job titles don’t always reflect the skills needed

AI Engineer, Applied AI Engineer, LLM Engineer and Machine Learning Engineer can mean very different things depending on the company. Without a clear understanding of the project, recruiters and hiring managers can end up searching for the wrong profile.

2. The most relevant experience is still developing

Some AI technologies are relatively new, so the number of candidates with several years of hands-on production experience is limited. Employers may need to consider people with strong transferable experience in software engineering, data or machine learning.

3. Competition for experienced candidates is high

Companies are competing for specialists who can deliver AI projects rather than simply experiment with new tools. These candidates often have several opportunities and can be selective about the projects, teams and compensation packages they consider.

4. Hiring processes don’t always match the role

A generic technical interview may not show whether someone can build and deploy an AI application. For specialist positions, practical assessments and discussions around real projects can provide a much better understanding of a candidate’s capabilities.

How to approach AI recruitment in 2027

If your business is planning to grow its AI or engineering team, a good starting point is to get specific about what you need.

Before opening a vacancy, consider:

• Define the business problem first. Are you building an AI product, integrating an existing model or automating an internal process? The answer will influence the type of engineer you need.
• Separate essential skills from nice-to-haves. Requiring experience with every AI framework or tool can unnecessarily narrow your talent pool.
• Look at transferable experience. A strong software engineer with hands-on LLM experience may be a better fit than someone with an impressive AI job title but limited production experience.
• Make the assessment practical. Give candidates the opportunity to demonstrate how they approach real technical challenges rather than relying only on theoretical questions.
• Move quickly and communicate clearly. Specialist candidates are often involved in several processes. Long gaps between interviews and unclear feedback can cost you good people.

For a deeper look at building technical teams, read our guide to scaling your recruitment team without increasing headcount.

Key takeaways

• Applied AI Engineers, Machine Learning Engineers and AI Infrastructure specialists are likely to remain important hires as companies move AI projects into production.
• Agentic AI is creating demand for engineers who can build systems that complete multi-step tasks and integrate with existing business tools.
• AI Solutions Architects and AI Product Managers will play an important role in connecting technical capabilities with business needs.
• AI security is becoming a more important consideration as organisations deploy AI across their products and operations.
• Job titles are not enough. Employers need to define the actual technical challenges and skills required before starting a search.
• A clear brief, realistic requirements and an efficient interview process can make a significant difference when hiring specialist AI talent.

Building your AI team? Let’s talk.

Finding the right AI talent takes more than matching keywords on a CV. You need recruiters who understand the technology, know where to find relevant candidates and can assess whether their experience fits your project.

At Global M, we help companies hire specialist technology and AI professionals across international markets. Whether you’re looking for one experienced AI Engineer or building a wider engineering team, we can support you through our Success-Based Hiring model or an Embedded Hiring Team of Talent Partners who work directly with your team.

Planning your next AI hire? Get in touch with Global M.