AI Engineer

Equal IT; Stockholms kommun; On-site; SEK 900,000 - 1,200,000; Full time

Job summary

Equal IT in Stockholm is seeking an AI Engineer to help shape the next generation of intelligent systems. You will work across the full lifecycle of AI products, with a strong emphasis on physics-informed approaches that combine data, models, and domain knowledge to guide engineering decisions.

The role calls for hands-on experience with large language models, agentic workflows, and deploying ML systems into production.

Qualifications

• Deep practical knowledge of LLMs and transformer models.
• Experience building agentic or multi-step AI workflows.
• Proven track record deploying machine learning systems in real-world environments.

Responsibilities

• Work across the full lifecycle of an intelligent system.
• Focus on physics-informed AI engineering.
• Collaborate with researchers and engineers to improve testability and reliability.

Skills

LLMs

Agentic AI workflows

ML deployment

Job description

Across engineering R&D, organisations generate vast amounts of experimental data.

Whether the challenge involves advanced materials, particle physics or complex engineering systems, the process often looks familiar:

An idea is formed → Parameters are selected → An experiment is run → Results are analysed → Someone decides what to try next.

But what if that decision could be made better?

What if an AI system could learn from historical experiments, combine that knowledge with physics and modelling, and help engineers answer:

“Given everything we know so far, what should we test next?”

We're looking for an AI Engineer in Stockholm to help us explore and build towards that future. To work across the full lifecycle of an intelligent system, with a particular focus on physics-informed AI engineering.

If you bring deep practical knowledge of LLMs, have built agentic or multi-step AI workflows and deployed machine learning systems into real-world environments, this is the role for you!

Here, there are enormous technical challenges to solve like:

How do you represent years of historical experiments? How do you combine data-driven models with physical understanding? How do you evaluate whether an experiment recommendation was useful?

And perhaps most importantly, how do you know what the system doesn't know?

We don't pretend to have all the answers. That's why we're looking for someone who is excited by the questions.

If you're an AI Engineer who wants to explore how AI can help shape the future of engineering and scientific discovery, we'd love to speak with you.