Recruitment

Navigating the AI Talent Landscape

As a recruitment company, we've been at the center of this shift as we help enterprises and startups navigate their AI hiring needs connected to their new business needs. We hope this blog can shed light on some of our challenges, strategies, and misconceptions about hiring AI talent.

Artificial Intelligence isn't new. But when powerful language models tools like ChatGPT hit the scene in late 2022, the way that we talk about AI and use it in our daily lives changed. From a company perspective, organizations are increasingly moving beyond AI proof-of-concept initiatives, and shifting their attention more towards actual AI production.

Just as the field of AI has evolved over the decades, so too has the demand for skilled machine learning engineers and specialists. Organizations with vast amounts of data have long recognized AI’s potential to reveal insights and drive innovation. However, the focus has shifted: today’s employers are seeking professionals with the latest, most advanced technical expertise to navigate the rapidly changing AI landscape.

Suddenly, companies everywhere were rethinking how AI fits into their overall strategy and scrambling to figure out what talent they needed to stay ahead of the curve. Traditional data-driven enterprises like financial institutions and logistics firms are now actively seeking talent skilled in reinforcement learning and natural language processing to build predictive models and conversational agents. Meanwhile, startups are pushing the boundaries of generative AI to disrupt industries ranging from entertainment to healthcare.

If you’re serious about bringing AI competence into your organization, you’re going to need more than just a good developer interested in AI. In other words, writing “it’s a big plus if you are curious about how we can leverage AI” into your job descriptions isn’t going to cut it.

The skillsets required—such as expertise in large language models, reinforcement learning, and generative AI—are still relatively new and not yet widespread. Finding candidates with these specialized skills remains a significant challenge for enterprises and startups alike.

Types of Companies Hiring AI Talent

Let's start with the basics - who is hiring AI talent right now? The short answer is everyone. The longer answer is that we see AI recruitment fall into two main categories:

AI-First Startups: These companies, such as Noteless AS or Aditto, build their entire business model around AI technologies. For instance, the Norwegian companies, Noteless leverages AI to enhance note-taking and organization for its users, making AI a core part of its product offering. These AI-native companies have different use cases, service offerings, and salary bands from their enterprise counterparts.
Enterprises Adding AI Competencies: Established organizations like Storebrand, Capgemini, Ruter, and Posten leverage AI to enhance their operations. For example, Storebrand uses AI to optimize financial advising, while Capgemini employs machine learning to streamline digital transformation projects. While AI isn't necessarily a part of their core business, they use the technology to make the most out of their data and improve their offerings. However, larger companies should have sufficient amounts of data, financial investment, internal capacity, and strategic vision to get the most out of AI.

Additionally, e-commerce, healthcare, and manufacturing companies are increasingly investing in AI talent to address specific needs, like personalization, diagnostic tools, and process automation. Organizations with vast datasets and clear AI use cases are the ones most likely to succeed in attracting top-tier talent.

Common AI Job Titles

One of the biggest hurdles in AI recruitment is navigating the myriad of job titles that flood the market. Some common titles you might see during your sourcing process include:

Machine Learning (ML) Engineer
Data Scientist
AI Engineer
Deep Learning Engineer
ML Ops Engineer

While these titles might look impressive, recruiters must dive deep into candidates' experiences to separate proof-of-concept work from real-world production deployments. For example, has the candidate built an internal tool, or have they worked on something tangible that clients use? The distinction matters.

In-Demand Skills and Qualifications

Like many technical fields, the skillset for AI talent has changed rapidly over the past few years. Even if a candidate has 15 years of experience, they might not have worked with the technologies needed for an AI role today. Sometimes, hiring a younger candidate with even a year or two of experience in the right technologies is better than hiring a senior backend engineer interested in AI.

With that in mind, there are a few key skill sets that we've seen transcend experience when it comes to AI talent. You should be looking for someone with a robust mathematics and statistics foundation. In companies like Facebook and Google, this is often complemented by a PhD. In addition, these technical skills are highly sought after:

Programming Languages: Python is quite common in the AI talent landscape, along with experience in TensorFlow, PyTorch, and Scikit-learn.
Data Engineering and Data Handling: Proficiency in managing large datasets and having experience with data pipelines, ETL processes, and data management tools (e.g., Apache Spark, Kafka) is an important skillset for your next AI hire.
Cloud Computing and Infrastructure: It’s valuable to be familiar with cloud platforms (such as AWS, Azure, or Google Cloud) and container orchestration tools (e.g., Docker, Kubernetes). This allows your team todeploy, scale, and manage AI models in a cloud environment.
Core Knowledge: Linear algebra, calculus, probability, and statistics are incredibly beneficial.
RAG (Retrieval-Augmented Generation): A crucial skill for working with large language models and vector databases. This is often a keyword to look for when you're sourcing.
Research Mindset: AI evolves rapidly, so staying updated with the latest developments is essential. To succeed in any AI role, they will need to be ready to proactively research and adapt to change.
Communication skills: We could write a sentence here about “communication,” where it is really important that a person can teach the rest of the technology/product department on how to use AI in development, etc, resulting in more efficiency.

Challenges in Sourcing AI Talent

Hiring top talent is filled with hurdles, and hiring top AI talent is no different. In addition to the "normal" hiring challenges, such as a limited talent pool, gender diversity, and hiring manager capacity, AI hiring is loaded with buzzwords, high salaries, and ambiguous assessment methods. Let's get into each of those and how we have tried to solve them in the past

Buzzword Overload: Differentiating true expertise from buzzwords can be trickier than you think. Many candidates claim AI experience, but the depth and breadth of that experience can vary immensely. You should be diligent when digging into their knowledge and getting into the details of the projects they have been working on. If they worked with ChatGPT, does this mean they technically have AI experience? If they are a backend engineer at a leading company interested in AI, does that mean they're equipped to step into an AI role and solve your company’s business needs?
High Salary Expectations: AI professionals command premium salaries, often pushing budgets to their limits. Since the talent pool is already so small, you're likely up against other companies with larger budgets to secure the same talent. If you're strapped from a salary standpoint, emphasize other perks of joining your team. For example, be clear about the use case of AI in your company, provide details about what sorts of projects and progressions the candidate can expect, and, as always, emphasize culture and perks. In other words, outline your AI strategy for the candidates to help sell your story.
Assessment Difficulties: Many AI skill sets are still relatively new to the market, and popular assessment methods and cases haven't quite caught up. Additionally, companies often lack the resources or expertise to accurately evaluate AI skills, making it hard to gauge a candidate's competency. You can always test experience within Python or other core tools, but we have found success with having an open discussion of expertise over a technical exercise. Program assignment around vector database LLM could be an option, but these aren’t used everywhere, and may only be one small component of their day-to-day. This is again why we opt for an open dialogue or even have the candidate join the company for a paid week trial period.

Strategies to Attract and Retain Top AI Professionals

To stand out in the competitive AI hiring market, you must have a clear vision and story around AI at your company.

Sell Your Vision: Be explicit about the AI use case and its strategic importance to your organization. Candidates need to see how their work will make an impact, so be concrete with the use case of the AI role. It's best to sell the idea that you're not here to just hire one AI guru, but you're committed to having AI as a core part of your strategy moving forward (if it isn't already, of course).
Focus on Potential: Don't over-index on experience. A candidate with a year of hands-on experience in the right technologies may be a better fit than a senior developer with only a passing interest in AI. And don't be afraid to challenge your hiring managers—if you're looking for all of the technical checkboxes to be ticked, you'll be disappointed since some of these tools are so new. Keep an open mind toward potential and learning adaptability.
Offer Growth Opportunities: Highlight opportunities for learning and working on cutting-edge projects. The best AI professionals thrive in environments where they can stay ahead of the curve, and will expect to grow quickly into their role and alongside the company. Emphasizing the growth journey you're on and how you will disrupt the industry can be a helpful edge in selling your story.

Misconceptions About AI Talent Recruitment

Recruiters and companies often fall into traps when hiring AI talent. Some common misconceptions include:

Assuming great software developers can easily transition to AI roles. AI requires a unique blend of skills that go beyond traditional software development. That's not to say that great software developers cannot become great AI engineers, but don't underestimate the nuances between these roles.
Jumping into AI without a clear strategy or sufficient data is risky. AI investments need robust datasets and clear use cases to deliver value.
Limiting the talent search to local markets. Expanding globally can unlock access to a more developed AI talent pool, especially for those in the Nordics as our tech talent pool is already limited.

The AI talent landscape is evolving rapidly. In the next 3-5 years, expect to see even more demand for AI-related skills across industries. We also see that developers are increasingly prioritizing AI-related roles, signaling a shift in the broader tech workforce.

Companies that adapt their recruitment strategies to this dynamic field will be best positioned to attract and retain top-tier talent. But building a strong AI team is all about having a clear vision, a thoughtful strategy, and the flexibility to adapt. It's not just about finding the right people—it's about creating an environment where they can thrive and innovate.

From crafting compelling job ads to assessing candidates' unique skill sets, recruiters have their work cut out for them in this competitive space. By focusing on concrete use cases, fostering learning opportunities, and expanding the talent search beyond borders, companies can unlock the full potential of AI—and their workforce.