Although AI proficiency is becoming a requirement across technology roles, employers are not paying a premium simply for experience with a popular model, coding assistant or prompt interface.
The greatest demand is for professionals who can turn AI prototypes into secure, reliable production systems and demonstrate how those systems improve the business, which requires technical foundations, knowledge of AI-specific architecture and the judgment to recognize when a model’s output cannot be trusted.
Draup analyzed approximately 2.85 million active job descriptions across nine engineering, data and AI-related roles between June 2025 and June 2026. The findings indicate employers are placing greater value on production experience, model oversight and senior-level decision-making than on familiarity with any single platform.
“The skills drawing the biggest premiums aren’t tied to one model or platform,” says Vishnu Shankar, chief data officer at Draup. “They’re the skills involved in putting AI into production and keeping it there: MLOps, deployment and monitoring, data engineering and governance, model evaluation, security, and reliability.”
While the tools have made some of the build work faster, he says the harder part comes after that: checking whether the outputs hold up, figuring out why a system is failing, and deciding what’s ready to go live.
“That’s where experience matters,” Shankar says.
Production AI Skills Command the Strongest Premiums
The ability to generate code or operate an AI tool is only one part of deploying the technology. Production systems require engineers who can design the architecture, prepare data, evaluate outputs, control access, monitor performance and respond when a model behaves unpredictably.
This places a premium on machine learning operations, or MLOps, along with data engineering, retrieval, model evaluation, AI security and reliability engineering. These competencies address the difficult work that begins after a team has demonstrated that an AI application can function.
“The premium is for engineers who already have strong fundamentals and know how to use AI to increase what they can deliver,” says Shams Chauthani, chief technology officer at Tempo Software.
That skill set combination is critical, as AI can accelerate development while simultaneously increasing the amount of code and infrastructure that teams must validate.
Coding agents may produce an application component quickly, but an experienced engineer must still determine whether it fits the architecture, introduces a security problem or will remain reliable under production loads.
While prompting prowess remains useful, it is unlikely to support a durable career by itself—the greater value lies in understanding context management, retrieval-augmented generation, tool calling and orchestration—and knowing when each approach is appropriate.
“AI can make a strong engineer dramatically more productive,” Chauthani says. “But it does not compensate very well for weak engineering judgment.”
Fundamentals Will Outlast Today’s AI Tools
Named AI tools are already appearing in job requirements, with a June Draup report finding GitHub Copilot, Cursor and Claude in more than 60,000 of the job descriptions it examined, including approximately 53,000 software engineering listings.
The results suggest IT professionals must develop hands-on experience with current tools, on top of software engineering, systems design, data architecture, statistics and cybersecurity knowledge.
Employers are also favoring candidates who can handle ambiguity and make decisions across an entire system. The Draup survey found senior, staff and principal variants grew faster than generic titles across every role in its analysis, while AI software engineer titles increased 57% year over year.
Greg Summers, CEO of talent acquisition firm Orion Talent, says lasting AI competency depends on learning agility as much as knowledge of a particular application.
“Rather than prioritizing specific AI expertise, professionals must have fluency to learn new technologies quickly and apply them to real business problems,” he says.
Employers Want Evidence, not a List of Certifications
Certifications can help early-career professionals establish foundational knowledge or give experienced workers a structured introduction to AI but are less persuasive when candidates cannot show how they applied that knowledge.
A strong portfolio should explain the problem, the selected approach, the safeguards used and the result. Candidates should be ready to discuss where a model failed, how they evaluated its output and what they changed before deployment.
“I would much rather talk to an engineer who can show me how they used AI to ship something faster, improve reliability or solve a problem that previously took much more effort than someone with a collection of AI certifications,” Chauthani says.
The business outcome does not need to be a dramatic revenue increase. Reducing processing time, identifying errors earlier, improving system availability or helping employees make better decisions can establish credible experience.
Employers also want to know which work the candidate delegated to AI, what they verified independently and where human judgment affected the result.
Summers describes this as a shift from credentials to credibility: Candidates who can quantify how they improved a workflow or solved an operational problem give hiring managers more useful evidence than those who merely list models and platforms.
Existing IT Roles Provide Multiple Routes Into AI
Software engineers, data engineers, platform engineers, site reliability engineers and cybersecurity professionals already possess many of the foundations required for AI careers.
Data engineers understand pipelines, lineage, quality and governance. The Draup survey found data quality in approximately 107,000 data engineering job descriptions and data governance in about 67,000. Those responsibilities become more important as organizations deploy more models that depend on accurate, traceable information.
Platform and reliability engineers can move into deploying, observing and controlling AI workloads. Security specialists will be needed to address prompt injection, excessive agent permissions, data leakage and other risks created when AI systems gain access to enterprise applications.
Summers notes Orion has seen a 35% increase in organizations hiring for critical software, data and infrastructure positions, including data architects, design engineers, technicians, field-service professionals and commissioning engineers.
“Existing IT roles offering the strongest path into durable AI careers are those rooted in managing complex infrastructure, managing trends and connecting technology to business outcomes,” he explains.
Professionals should build from their existing discipline while gaining practical experience with AI tools, rather than attempting to reinvent themselves around a temporary job title.
“The exposure isn’t in learning the tools. It’s in learning only the tools,” Shankar says. “Someone who understands the work underneath picks up the next one in a week.”
