The rise of 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴.

What is this role really about?

I’ve recently outlined my thoughts in an episode of SwirlAI Newsletter.

In my opinion the role best fits generalists that posses a blend of skills from:

➡️ AI Researcher.

➡️ ML Engineer.

➡️ Software Engineer.

➡️ (Potentially) Data Engineer.

Unfortunately, it is naive to expect professionals to have fluency in all of them. My short term prediction is that most of the specialists in the role will have a blend of two, falling into one of the categories 𝟭. to 𝟯.

Surprisingly, there is a lot of non-engineering related work in your day-to-day life as an AI engineer. The organisations are still on a hype train around LLMs, and you would be pushed to implement the technology wherever, even if there is no good fit. One of your key responsibilities will be to coach the organisation and be at the forefront of deciding where and if GenAI is necessary to solve a business problem.

AI Engineer is positioned to be the hottest role in the upcoming years. The salaries are high and the demand will keep increasing due to the shortage of talent in the field. 2025 will be the year of agents, 2026 - very likely the year of multi agent systems and autonomous agents.

Yous can find the article here:

I put my thoughts on:

➡️ Why the new role is justified. I ground the reasoning in how LLM based apps have evolved in recent years.

➡️ What it would take to become an AI Engineer.

➡️ The future of the role.

❗️ Bold prediction: most of the engineers will be developing full stack in ~2 years. Every one of them will need to possess skills of what we call an AI Engineer today.

Let me know your thoughts about the role! 👇