The 2025 AI Hiring Playbook

To present the insights in this report, Dice used job posting data provided by Dice’s partner, Lightcast, which has a database of more than 1 billion current and historical job postings worldwide. Dice pulled data on June 5, 2025 and analyzed tech job postings in the U.S. using Lightcast’s skills category taxonomy specific to “Artificial Intelligence and Machine Learning (AI/ML)” and “Natural Language Processing (NLP)”. The AI/ML subcategory contains 301 skills (up from 120 in 2024) and the NLP subcategory contains 65 skills (up from 44 in 2024). The information in this report is a snapshot of tech job posting data as of June 6, 2025 and backward revisions to prior month’s data may occur from the sources used in this report.

Your competitors think AI hiring is about finding data scientists, and they are struggling with a limited talent pool. Meanwhile, smart recruiters are quietly placing a broad range of AI candidates at 18% salary premiums. The artificial intelligence job market has evolved into something completely different from what anyone expected. The firms that come in with the right strategy are building massive competitive advantages while everyone else chases the wrong talent.

In May 2025, over one third of all tech job postings required AI skills, a new peak to a trend that has shown no sign of slowing since it took off in early 2023. Does this mean more demand for tech professionals who can build and implement AI? Or does it mean that AI skills are now required even in more traditional tech roles?  In this chapter, we look at the various ways AI skills are being integrated into new jobs on the market, and how you can communicate better with hiring managers on the subject.

AI has gone from emerging trend to essential skillset in record time. Today, professionals across tech are expected to understand and apply AI in their roles. However, AI roles are as broad and varied as general tech roles ever have been. Knowing what kind of AI expertise a role really demands can save hours of wasted sourcing and open up entirely new talent pools.

To make some sense of this, we have split this group into three distinct categories of AI talent.

AI Builders: The Technical Backbone

The first group of AI roles are filled by the people who can build AI systems, typically carrying job titles such as “machine learning engineer,” “AI researcher,” “data scientist,” etc. These experts could be focused on model development, or they possess specialized skills around training and deploying AI models. These professionals likely have advanced degrees in computer science, mathematics, or related fields, and their work involves the mathematical and computational challenges of making AI systems function.

The talent pool here is relatively small and highly concentrated. AI Builders typically came up through academic research programs or have spent time at major tech companies working on AI infrastructure. They speak in terms of algorithms, training datasets, and model architectures. When they evaluate opportunities, they’re often going to weigh factors like research freedom and the technical complexity of the problems they’ll be solving.

What’s interesting about hiring experts from this segment is how it resembles academic hiring. Publications, conference presentations, and open-source contributions may matter more than traditional software development portfolios. Compensation packages frequently include conference budgets and continuing education allowances.  These AI Builders may even have expectations around intellectual property rights that would be unusual in other technical roles.

AI Orchestrators: The Business-Technical Bridge

Our AI Orchestrators group consists of people who understand the tech behind AI, but focus on the implementation and business integration of an existing model. In other words, they deploy, govern, and scale AI to meet the needs of a business. Jobs within this category include prompt engineers, AI product managers, implementation specialists, and the various “AI strategist” positions that have emerged over the past two years.

Many of these AI Orchestrators come from traditional tech or business roles but are working to angle their career into the AI space. They’re the people who can evaluate when an AI solution makes sense for a business problem, manage the integration of AI tools into customer facing products, and communicate between AI Builders and other stakeholders.

Organizations need people who can think strategically about AI applications while understanding enough about the tech to make realistic implementation decisions. With all that is changing in AI, you can see why this doesn’t map neatly into traditional career paths.

Compensation in this segment varies widely depending on industry and company size, but these roles often command premiums because they’re critical to successful AI adoption. Companies have learned that having great AI technology without someone who can orchestrate it results in failed implementations.

AI-Enhanced Professionals: The Business Accelerators

Hiring AI-enhanced Professionals to fill traditional roles is just breaking the surface, but it is looking more and more like the future every day. This group represents traditional tech professionals who’ve integrated AI into their workflow to improve their own quality and efficiency. Software developers using GitHub Copilot, data analysts leveraging automated insights, QA engineers with AI-powered testing suites all fall into this category; they might seem like “vibe coders,” but they have the provable technical experience to leverage AI into a usable product.

This category is where most of the actual hiring volume exists, though it’s often not recognized as “AI hiring” in the traditional sense. These professionals typically don’t have experience building AI systems, or the ability to implement models directly into products, but they’ve developed marketable fluency with AI tools by using it in their day-to-day tasks.

Some clients might not be sure they need “AI-enhanced professionals” quite yet, and you can demonstrate expertise by helping them get here. When they say “AI software engineer,” they mean “developer who can work with our AI tools.” Getting this distinction right will open your candidate search up to some of the most innovative, adaptable talent out there.

Prompt engineering skills grew 219% year-over-year, but “Prompt Engineer” job postings only increased 77%. What’s happening? The skill is becoming a baseline requirement across multiple AI roles rather than a standalone position.

What this means for recruiters:

Don’t search for dedicated “Prompt Engineers”. Instead, look for traditional tech professionals—product managers, developers, implementation specialists—who’ve added prompt engineering to their toolkit. The market has moved from hiring prompt engineering specialists to expecting prompt engineering competency in broader tech roles.

AI skills command premium rates, and budgets are following. According to our 2025 Tech Salary Report, this premium can be up to 18%. As a result, AI hiring has become competitive and expensive.

Supply and demand imbalances are the most obvious factor. The number of organizations trying to implement AI solutions has grown faster than the number of professionals with relevant experience. This is particularly acute in the AI Orchestrator category, where the required skill combination is new enough that there aren’t established educational pathways or strong networks of circulating candidates.

Geographic distribution also plays a role. AI talent tends to be concentrated in major tech hubs such as Silicon Valley and New York City, but demand has become more geographically distributed as organizations across different industries adopt AI solutions that need to be implemented.

Competing Market Pressures

This comes at a time where unemployment and remote work are contentious subjects in the tech community. On one hand, companies are implementing return-to-office mandates and layoffs among their tech workforces. On the other hand, they are fighting to attain the AI talent needed to compete with their competitors and implement solutions that excite shareholders.

Perhaps most significantly, the integration complexity of AI implementations has made organizations willing to pay premiums for professionals who can help them avoid costly mistakes. Failed AI projects are expensive in terms of both direct costs and opportunity costs, so organizations have become willing to invest more in talent who can increase their chances of successful implementation.

The result is a market where AI-related skills command significant premiums across all three talent tiers, but where the specific premiums and their drivers vary depending on the role type and implementation context.