I am delighted to present The AI Engineering Skills Map. AI allows us to build software very differently today than in 2022, and everyone with the skills to take advantage of this shift has numerous exciting project and job opportunities. But with the noisy, hype-filled, information environment around AI, what are the most valuable skills for you to learn? I have been working with my team to synthesize a map of AI engineering skills in order to help (i) developers prioritize what to learn, and (ii) employers hire skilled developers.
Based on an analysis of over 10,000 job postings; carrying out dozens of structured interviews with AI experts, hiring managers, and recruiters; gathering data through surveys; and synthesizing other online data, here are the four most important AI engineering skills:
Building and deploying AI applications
Software engineering fundamentals
Using coding agents
Shaping the build
You can informally think of our process as akin to running clustering on a massive dataset of jobs and expert interviews to identify the most important skills, not just today but also in the near future.
A note on terminology: I talk about AI Engineering skills rather than the “AI Engineer” role (someone whose job is to build AI systems), because the former is much broader. All developers today should know how to work with the cloud, and only a smaller number have a “Cloud engineer” title. Similarly, all developers — full-stack engineers, data engineers, DevOps engineers, machine learning engineers, and, yes, AI engineers — will need AI engineering skills.
Building and deploying AI applications. The key difference between AI and non-AI applications is that the former has unpredictable outputs. When you prompt an LLM, you don’t know what you’ll get back. When you train a deep learning algorithm, you don’t know what prediction it will make on new examples. In contrast, traditional software behaves more predictably.
People who are skilled at building and deploying AI applications understand the building blocks of AI (such as LLMs, context engineering, RAG, agentic workflows, machine learning and deep learning) and, importantly, how to use statistical techniques to measure, steer, and govern AI systems so that they behave more predictably. A core skill in doing so is knowing how to drive disciplined evals and error analysis loops.
Software engineering fundamentals. When you deeply understand how software works, you can build much more effectively. Engineering software requires making tradeoffs between cost, scalability, reliability, speed, and more. Security and privacy add further complexity.