5 AI Jobs in Sharp Demand—and the Skills Needed

AI is now part of a broad range of roles, but it’s tricky to know which specific skills are needed for which roles.

AI skills. Companies want them. Job candidates claim to have them. But it’s tricky to know which skills are needed, and for which roles. 

Job openings directly related to generative AI are up 42% this year; functional positions requiring AI skills—including roles in sales, finance, and marketing—are up even more. On the candidate side, LinkedIn reports that profiles mentioning AI skills are up 142% in the last year. The problem, say experts, is that both job postings and résumés use vague language—like “extensive experience with AI modeling”—that leaves hiring managers and recruiters confused. “AI is now part of a wide swath of roles,” says Paul Fogel, professional search sector leader for software at Korn Ferry, “but that doesn’t mean everyone needs to know how to write code or build the platform themselves.”

To put it another way, a machine-learning engineer needs an entirely different skill set than a CFO whose department is rolling out new AI tools. Here, we break down the AI-related expertise required for certain key positions.

Platform engineers

Increasingly, companies are opting to build their own AI systems rather than use a third-party platform. This creates a need for engineers who can create the infrastructure internally. Platform engineers build the environment to train AI, including creating data pipelines and machine-learning algorithms. “If AI models are fish, platform engineers build the aquarium,” says Fogel. 

Key skills: Expert-level knowledge of programming and software development, machine learning and data science, cloud computing, systems architecture, cybersecurity, and regulatory compliance. 

Machine-learning engineers

Once platform engineers build the infrastructure, machine-learning engineers populate it with AI models and tools, creating algorithms to teach the technology how to respond to different prompts. “Machine-learning engineers are crucial for optimizing AI models for performance,” says Deepali Vyas, global head of the FinTech, Payments, and Crypto practice at Korn Ferry.

Key skills: Proficiency in programming languages like Python or R, as well as experience with machine-learning frameworks such as TensorFlow and PyTorch. Other important technical skills include statistical modeling and data collection, analysis, and storage using SQL, Hadoop, and others. 

AI product managers

AI product managers are in demand to help establish the vision and strategy for AI tools internally and externally, Vyas says. They serve as a bridge between building and using AI, and as such require relevant technical skills; these don’t necessarily include deep expertise or training. “The focus of roles like this is on how AI can improve business processes, drive innovation, and provide strategic insights,” she says.

Key skills: Basic understanding of programming languages and machine-learning algorithms. Main skills needed are those of traditional product managers, such as aligning product with market opportunities and business needs, leading the development road map, managing the launch and go-to-market strategy, and analyzing performance. 

Prompt engineers

It’s probably the most mainstream AI-related job. Prompt engineers refine the instructions, or “inputs,” that teach an AI model how to answer. Prompt engineering aims to generate the most accurate, relevant, and useful responses. It generally requires a basic understanding of AI concepts, but deep technical knowledge isn’t necessary. 

Key skills: Knowledge of or experience with programming languages such as Python and databases such as SQL; also, more broad-based skills such as communication, collaboration, creativity, copywriting, subject-matter expertise, and analytical thinking. 

Functional leader roles

Fogel notes that “pretty much every role now requires some degree of AI literacy.” Companies are increasingly requiring leaders to have AI skills in such functional areas as finance, sales, marketing, and human resources. Knowing what work to automate, as well as which AI tools to use, how to train people on them, and how to integrate them into operations is the “new standard for functional leaders,” says Fogel. “Companies now want leaders they hire to be far more AI savvy.”

Key skills: Knowledge of and experience with domain-specific AI platforms and basic data-analysis techniques such as audience segmentation and behavioral trends. Programming, coding, and other, more technical skills are normally not required for leadership roles outside of digital and information technology.


For more career advice, connect with a career coach at Korn Ferry Advance.