What Is an AI Engineer and How Has the Role Changed?

‍LinkedIn ranked “AI Engineer” as the number one fastest-growing job in the US in their 2025 report, a consistent trend over the past three years. Pave’s Hot Jobs Index ranks AI Engineer as the hottest job in our dataset as of mid-2026. But, even though the title is on the rise, there’s still ambiguity around responsibilities, requirements, and what separates an AI Engineer vs. Software Engineer. 

For compensation professionals, understanding those nuances matters. Getting the role right informs hiring, retention, and benchmarking strategies.

Let’s explore the origins of the AI Engineer, how the role has evolved, and what Pave data shows about how the market is pricing it today.

AI Engineer vs. Software Engineer: What’s the Difference? 

To understand the role of an AI Engineer, it’s helpful to compare it to other similar positions. 

Here’s how AI Engineer, Software Engineer, and AI Research Scientist jobs differ. 

• Software Engineers design and maintain the software systems and applications that power new technologies.
• AI Engineers integrate AI into those systems and deploy existing models to build production-ready features and products.
• AI Research Scientists develop new models from scratch, pushing the boundaries of what AI can do.

The bottom line?

Research Scientists invent new models. Software Engineers code business applications. AI Engineers bridge the gap between the two, bringing AI models to new products and systems.

How Has the Definition of an AI Engineer Evolved Over Time?

Before the generative AI boom, much of what AI Engineers do today existed under other titles, such as Machine Learning Engineer or Applied Scientist. 

The introduction of tools like ChatGPT and Claude made APIs more accessible and created a new category of engineers who built on top of pre-trained models. 

According to Andrej Karpathy, co-founder of OpenAI and former Director of AI at Tesla, “There’s probably going to be significantly more AI Engineers than there are ML/LLM engineers. One can be quite successful in the role without ever training anything.”

This change reduced the barrier to entry and increased demand, as businesses raced to develop new AI functionality—and Pave’s data reflects that growth trajectory. In January 2023, just 2.7% of companies in Pave's dataset employed at least one AI Engineer. By January 2026, that figure reached 8.4%.

What Are the Requirements to Be an AI Engineer?

AI Engineers are responsible for applying and deploying AI and agents, not researching or inventing them. The role is part builder and part translator, taking complex business problems and turning them into AI-driven solutions.

That requires a combination of technical and soft skills. AI Engineers are expected to know programming languages, like Python and TypeScript, and work cross-functionally with product and engineering teams to ship reliable and production-ready solutions.

Across AI Engineer job postings, a few patterns emerge consistently. The core technical requirements are generally: 

• Python proficiency
• Experience working with LLMs and APIs 
• Familiarity with deployment infrastructure—RAG systems, agentic workflows, and evaluation frameworks appear frequently at the senior level. 

But what distinguishes AI Engineer postings from traditional Software Engineer roles is the cross-functional scope: these roles are consistently expected to translate between business requirements and technical implementation, partnering with product and design rather than operating purely within an engineering org. The role is as much about judgment—knowing when and how to apply AI to a given problem—as it is about technical execution.

What Does AI Engineer Pay Data Tell Us?

Understanding what the role actually demands is the foundation for benchmarking it accurately—which is where the pay data gets interesting.

We’re seeing the pay premium associated with AI Engineers narrow. When comparing AI engineer vs. software engineer compensation, the median base salaries at equivalent job levels in the US are close to equal—a situation that wasn’t the case a few years ago.

This signals that AI fluency is becoming an expectation for engineers, rather than a specialized skill. Many companies are redefining what it means to be a software engineer, rather than creating net-new AI Engineering job families. 

But there is still a stark difference between AI Engineers and AI Research Scientists. Pave data shows that AI Engineers earn approximately 19.9% less than AI Research Scientists. 

The Path Forward

The AI Engineer role is growing, but our research suggests it may just be the next evolution of the Software Engineer, rather than an entirely new job family. As AI fluency becomes a baseline expectation, the two roles are becoming one and the same.

We’ll continue to monitor the data and update the Hot Jobs Index with new findings. 

For even more insights, join Pave Data Lab. 

Participants get access to interactive analysis on topics like AI and ML compensation trends, unvested equity holdings by tenure, and new hire vesting schedules.