Applied AI Engineer
Legora is hiring an Applied AI Engineer in Stockholm, Sweden. Level rates it ; you can apply on Level.
AI in this role
Build internal AI agents, tools, and platforms to make legal workspace systems AI-native by default.
About Us
Legora is redefining how legal work gets done. Not built for lawyers, built with them. We work alongside the world’s best legal teams, who expect excellence, precision, and speed, and we hold ourselves to the same bar.
Our AI-native workspace lets legal professionals move faster, think more clearly, and operate with sharper precision. By analysing thousands of documents in minutes and powering end-to-end workflows, we cut through complexity, teams can focus on what matters: judgment, strategy, and outcomes.
2,100+ customers across 80+ countries trust us, including Cleary Gottlieb, Goodwin, Linklaters, White & Case, Dentons, and Barclays. We’ve scaled to $200M+ in ARR, with teams across Europe, North America and APAC, and continue to expand through acquisitions including Qura, Walter AI, Graceview, Cadastral, and Wexler.
We partner with world-class performers: including Aaron Judge and the New York Yankees, Ludvig Åberg (and his caddie), and campaigns featuring Jude Law.
Joining Legora means three things.
We lean in: ownership over titles, outcomes over intentions.
We fight for excellence: high standards, direct, ego-free feedback.
We grow together: as a team and with our customers.
Mission before ego. Everyone contributes. No one coasts.
If you’re driven by impact, pace, and raising the bar. This is the place.
Stockholm | Full-time | Onsite, five days a week
The team
AI Enablement makes Legora AI-native by default. We work across three streams in parallel: building internal agents and tools, equipping teams to build their own, and owning the standards, access and guardrails that let everyone move fast safely.
We own the AI platforms and the internal products built on them. So far that includes Leya, an internal knowledge agent live in Slack that answers questions about Legora with cited sources and automates parts of our RFP and security questionnaire work. A structured, agent-readable knowledge base assembled from knowledge each domain continues to own, where agents propose changes and humans approve them. Leyf, a platform for internally built apps that lands anyone's app on shared infrastructure with guardrails scaled to how much damage it could do. And a Skills Hub, so that something one person works out becomes something the whole company can use rather than staying on their laptop.
The team is small and the mandate is large. That is the job.
What makes this hard
Legora is an AI company of around a thousand people, most already fluent and on current tools. Internal AI here is not an adoption problem. Nobody needs convincing, which means there is nowhere to hide behind change management or training. If something we build is not used, it is because it was not good enough.
Our colleagues know what good looks like and will not use something worse than what they think they could build themselves in an afternoon. So we ship things people genuinely want, and the foundations arrive with them. That constraint does not go away.
The mandate is also larger than the team. The part no individual can do for themselves is the infrastructure, the access model and the architecture underneath, and getting that right across a company this size is the actual work. It does not come with a specification.
What you will do
You will take broad ownership of how AI works inside Legora and of the platforms we build to make it work.
Build internal agents, tools and platforms, and treat them as products rather than projects. Quality, reliability and whether they actually serve the business are yours, not someone else's
Take a broad goal, work out what is actually required, and break it into the work yourself
Own what you build end to end, from the architecture through to whether people use it and it holds up in production
Shape what we should build and in what order, and argue for it
Design the foundations: knowledge architecture, access and permissions, evaluation and monitoring, the guardrails that scale with risk
Work directly with the people who will use what you build, across the whole company, including the parts furthest from engineering
Who we are looking for
Strong engineering fundamentals. You build production systems, not prototypes that need someone else to finish them
Real depth with LLM-based systems. Agents, retrieval, evaluation, tool use. You know where these systems break and you design for it
Ownership by instinct. You do not wait for a specification. When the requirement is unclear or wrong, you say so and propose something better
Three kinds of understanding at once. Technical, domain and business. You can hold a conversation about architecture and a conversation about what the business actually needs, and connect them
Openness. You take feedback, build on other people's ideas, change your mind when the evidence moves, and push for the strongest outcome rather than the one you proposed first
Comfort with ambiguity. New team, fast growth, decisions that get revisited as we learn. That should sound appealing rather than annoying
TypeScript. It's what we work in, and where our shared patterns live
No aversion to infrastructure. We own ours, so Terraform and cloud providers are part of the work rather than someone else's problem
You work with code agents daily. Not as an experiment, as how you build
Legora is an Equal Opportunity Employer
At Legora, we believe great teams are built on diversity of thought and experience. We’re proud to be an equal opportunity employer and committed to creating an inclusive, high-performance culture where everyone can do their best work. We welcome people of all backgrounds and don’t discriminate based on race, color, religion, national origin, gender, gender identity or expression, sexual orientation, age, disability, veteran status, or any other characteristic protected by law.
How we rate this
Applied AI Engineer at Legora rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in AI engineer jobs at this level
Pay
- Typical AI engineer job
- $183k
This posting doesn't list pay. Use the typical AI engineer figure when pay comes up. See AI engineer jobs
Questions you could be asked
- Tell me about a project where machine learning was part of your work. What did you do?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Tell me about a project where software engineering was part of your work. What did you do?
- Tell me about a project where llms was part of your work. What did you do?
- Walk me through how you've used Python in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Machine learning, AI agents, Software Engineering, LLMs, and Python. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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