New technological challenges give rise to new professions. Every time a new group of professionals emerges with a completely different background, speaking a different language, producing a completely different set of products, and using a completely different set of tools, a new profession is born.

This very thing happened recently with “Site Reliability Engineers”, “DevOps Engineers”, “Data Engineers”, and “Analytics Engineers”. Similarly, the new challenges of AI are starting to produce new engineers with distinct features and tools:

– UX for AI applications.

– AI application development tools.

– AI application infrastructure.

– AI agents.

– New LLM tools, including Langchain, vector databases, etc.

– Open-source models (training, fine-tuning, inference, evaluation, etc).

– Etc.

These engineers don’t yet have a standardized title, although in the United States the term “AI Engineer” is starting to be used to refer to them. Other names like LLM App Developer, LLMOps Engineer, or AI Manager are also used.

Today, there are still ten times more job offers for classic ML engineers than for the new AI engineers, but the rapid growth of the latter suggests that this ratio will invert soon.

As recently acknowledged by Andrej Karpathy, one of the most influential engineers in the AI world, these are likely to be the most in-demand professions of the next decade.

From ML Engineer to AI Engineer.

While classic ML focuses on tasks such as fraud detection, recommendation systems, and product anomaly detection, the new AI engineers build text-based applications, personalized learning tools, natural language spreadsheets, or visual programming languages like Factorio.

A Salary of $900,000 a Year.

The top AI engineers earn close to $900,000 a year at OpenAI. More and more companies, including Microsoft, Google, Facebook, and Tesla, are entering the bidding war for this new professional segment.

The new AI engineers are applying the latest advancements in this field and turning them into real products used by millions of users virtually overnight. This is happening both in large corporations and in cutting-edge startups like Figma, Vercel, Notion, Photo/InteriorAI, or Scale AI.

In the vast majority of cases, when it comes to developing AI products, companies are looking for engineers, not researchers.