Senior Applied AI Engineer

We’re seeking a driven Senior Applied AI Engineer to join one of the fastest-growing legal tech companies in Europe as we expand our core AI team.

This role focuses on applied generative AI in a production environment, building systems such as RAG pipelines, agentic workflows, and AI features used directly by lawyers.

Reporting to the AI Lead, you’ll work hands-on from architecture to implementation, collaborating with the Head of Engineering and product teams to ship reliable AI functionality while improving our LLM infrastructure, observability, and evaluation workflows. You'll join an AI squad of five Applied AI Engineers, the AI Lead, an AI PM and legal engineers.

For an engineer with practical experience building and deploying generative AI systems, this is an opportunity to create real product impact and help shape how AI evolves across the company.

Your Challenge and Opportunity in This Role

You’ll help define how AI is built and shipped in a fast-moving product environment. As a core AI Engineer, you’ll turn modern generative AI techniques into production-ready systems that deliver real value to users.

You will contribute to capabilities such as document understanding, document generation, and conversational interfaces over legal case files, and Jara, our agentic legal assistant, which preps each lawyer's day, requests missing documents, drafts briefs and creates case files, with the lawyer approving every action. Expect strong technical ownership, fast iteration, and a clear focus on building practical AI systems that improve productivity for law firms.

Tasks

AI Strategy & Implementation:

• Architect and implement production-ready generative AI and LLM-based solutions within our product suite

• Design and develop agentic systems like Jara that perform multi-step casework within the JUPUS platform, with action previews, clarifying questions, approval gates and full traceability

• Build and maintain RAG pipelines enabling users to interact with documents and case files

• Implement evaluation, testing, and observability for AI systems to ensure reliability and continuous improvement

• Establish best practices for AI development, testing, and deployment across products

• Initially handle AI