Skilled AI and ML workers are the hottest commodity in the IT job market
Countless tech industry experts have warned that the rapid adoption of artificial intelligence will be a huge disruptor to the workforce, especially IT roles. But the reality is, that ship has long since sailed.
Indeed, it now seems obvious that the IT job market is undergoing a major shift. As AI automates entry-level coding and routine tasks, the traditional paths into technical careers are disappearing. Meanwhile, recent research from International Data Corp confirms that at least a foundational knowledge of AI capabilities is now required of virtually every IT hire. That means that job opportunities for IT pros with AI or machine learning experience are virtually unlimited. But for IT roles that don’t require AI and ML skills, it may be a very different story.
This split in the IT workforce leaves new graduates with credentials but fewer ways to gain real-world experience, says Stephanie Newland, director of workforce development at technology services firm TEKsystems Global Services. At the same time, many organizations continue to face an urgent talent shortage: 90% say they don’t have the skills needed to drive transformation, according to TEKsystems’ 2025 State of Digital Transformation report.
“The challenge isn’t a lack of opportunity; it’s a mismatch between where demand is rising and where talent pipelines are thinning,” Newland explains. “To win out, professionals need to be able to use their technical skills while showcasing a willingness to learn in AI-first environments. At the same time, employers must invest in robust training initiatives to prepare this next wave of talent and support their technology ambitions.”
A ‘healthy but uneven’ demand for AI and ML pros
As to the strength of the overall IT job market, the short answer is that it’s healthy but uneven, says David Case, founder and president of Advastar, a recruiting firm focused on the energy, manufacturing, and construction sectors. Case says his firm has been increasingly called on to hire IT talent for clients to support their digital transformations, particularly professionals with skills in automation, data analytics, and cybersecurity.
“Employers have been cautious with their hiring of late, but there is a persistent demand for people who can apply AI to real-world business problems,” Case explains. “Even companies that have intentionally slowed hiring are still filling roles related to specific skills they need to add to their workforce, and strategic application of AI is one of the top areas where we’re still seeing robust demand.”
Confirming that assessment is Kanani Breckenridge, CEO and ‘headhuntress’ at San Diego-based recruiting firm Kismet Search, “AI and ML represent the strongest hiring demand I’ve seen in any technology sector, similar to the dot com boom 25 years ago,” Breckenridge says. “The talent shortage is acute for candidates with any depth in these skills, with qualified candidates typically receiving multiple offers within days of becoming available. Organizations across industries are competing for the same limited pool of experienced professionals, and a lot of the bigger companies that can afford to pay more are often winning out over smaller organizations and startups.”
Competition for skilled AI talent is so strong that we’re in the middle of an AI hiring boom the likes of which we will probably never see again, Baden says. The closest parallel is the 2021 software engineering hiring frenzy. There are simply way more open roles than there are qualified candidates, Baden says.
Top demand is for ‘last mile’ AI engineers
While the IT job market for AI and ML roles is strong in general, the opportunities look different from what one might expect, says Tim Mobley, president at staffing firm Connext Global. “We’re seeing AI automate 70-90% of a workflow. The last mile still needs people. That’s what’s driving hiring for roles like AI workflow managers, automation QA analysts, model evaluators and prompt engineers.”
As AI tools replace repetitive coding, organizations are looking for professionals who can drive higher-value outcomes: AI integration, governance, and human-AI collaboration. Newland says this shift is creating opportunities in areas that didn’t exist five years ago, but it also raises the bar for entry. The demand is there, but training and readiness haven’t necessarily kept pace.
Specific to machine learning roles, Breckenridge says a top need is for machine learning engineers who can handle end-to-end model development and production-scale deployment. MLOps engineers are also in demand because companies have learned that research prototypes don’t automatically scale to production systems. So they want MLOps engineers to handle the infrastructure and security work needed to deploy at an enterprise and commercial level. AI product managers are increasingly important as organizations struggle to translate technical capabilities into business outcomes and ROI for potential customers.
The most in-demand skills for AI and ML hires
In terms of desired skills, Breckenridge says Python proficiency is baseline, with Go, Rust, React and Typescript as the most popular languages. Real differentiation comes from experience with cloud-native ML deployment, understanding of production monitoring, and the ability to optimize models for both performance and cost.
AI Engineers focused on agentic AI architecture and LLM application development have an edge in the market currently. And machine learning engineers and AI engineers who have skills in productionizing models and building pipelines are highly sought after, Case says.
“We’re also seeing strong demand for MLOps and site reliability professionals, ML infrastructure engineers, applied data scientists, and AI product managers,” Case explains. “In terms of skill sets, there is a high demand for candidates with LLM and foundation model experience, skills in data engineering – Kafka, Airflow, data lakes, vector DBs – and production machine learning – model deployment, monitoring, and drift detection. It’s especially valuable if candidates combine these skills with domain expertise in the specific manufacturing or energy sector where the employer operates.”
Otherwise, Newland says we’re seeing demand for roles such as:
AI governance and ethical oversight
Prompt engineering and human-AI interaction design
Cloud-native platform integration
Data science with a focus on customer experience
Cross-functional product and design roles that bring AI into real-world applications
“The common denominator is AI knowledge paired with human-centered capabilities like problem-solving, communication and the ability to work across functions,” Newland explains.
AI and ML pros can demand top pay in the IT ranks
Considering the high-demand for skilled AI and ML pros, potential compensation is quite generous.
Senior-level AI professionals with five-plus years of experience can command $200,000 to $300,000 base salaries, and depending on the market they are in, often much higher with significant equity and bonus components, Breckenridge says. The compensation inflation reflects genuine scarcity with knowledge that replacing AI talent is both extremely difficult and expensive.
Nearly any roles related to AI and machine learning continue to demand higher base salaries. Case says he has seen these offering up to 30% higher salaries compared to similar software roles, depending on their skill level and location. Benefits are a major piece of this puzzle, as well, particularly in markets that have struggled to attract top candidates. These often include sign-on or relocation bonuses, learning stipends, and long-term incentive programs such as equity or stock units or performance-based bonuses.
“Cash salaries are high, but the real battleground is equity. Startups are dangling equity like candy to secure scarce AI talent,” Baden says.
Remote work flexibility has also become standard rather than a perk, but a hybrid work arrangement is more common. Companies that insist on full-time office presence are having a harder time attracting top talent, Breckenridge says.
It should be noted that actual compensation rates are based on impact. Employers are paying premiums when roles tie directly to results, such as customer safety, compliance, and revenue conversion, rather than just technical skills. Benefits are also evolving. Workers increasingly expect flexibility, parity across global teams, and recognition as part of culture, not just a technical function, Mobley explains.
Sizing up the ideal AI or ML job candidate
The most competitive AI or ML candidates combine deep technical skills with business-side and communication aptitudes. Candidates who understand not just how to build models, but how to explain their value to non-technical stakeholders and integrate AI solutions into existing business processes are particularly in demand. Due to the nature of creating successful algorithms and models, there is more emphasis on the caliber of their education than in the past, Breckenridge explains.
Ideal job candidates are those who bring a combination of deep technical skills and hands-on knowledge of a particular industry domain, says Maitreya Natu, chief data scientist at AI services provider Digitate. Such profiles are able to make the best of AI to address problems specific to an industry.
“There is growing demand where organizations are trying to go ‘AI native’ in all aspects of their operations,” Natu explains. This requires data engineers and data architects to make organizational data AI-ready. There is also demand for AI product managers and AI architects to enable creative ways to using AI in all aspects of operations.
The strongest AI and ML job candidates are also adaptive learners, Newland says. They understand the potential and the limits of AI, and they bring creativity, ethics, and cross-disciplinary thinking to the table.
“They’re comfortable in collaborative, fast-moving environments where technology is constantly reshaping the work itself,” Newland says. “In other words, they don’t just know how to use the tools, they know how to make the tools work with people and organizations.”
