647 geprüfte KI-Jobs

Anforderungen

Technical Expertise: - Extensive hands-on experience building and deploying generative AI systems in production - Strong experience with RAG architectures (embeddings, retrieval, ranking) - Practical experience designing and deploying agentic systems or AI agents in production - Experience with LLM observability and evaluation tools (e.g., LangSmith, Langfuse) and designing evaluation pipelines for LLM systems - Strong programming skills in Python and solid software engineering practices - Experience designing LLM systems beyond simple API integrations, including retrieval pipelines, orchestration logic, and tool/agent workflows - Experience implementing AI features in real product environments, not purely research or academic settings - Ability to translate modern AI techniques into reliable, user-facing product functionality

Das wird geboten

Foundational Impact – Help build and scale the core AI capabilities of JUPUS from the ground up Technical Ownership – Work directly on the systems powering AI features across multiple products Growth Environment – Build production AI systems at the cutting edge of generative AI and agentic workflows Leadership Visibility – Flat hierarchy with direct access to the Head of Engineering and CTO, reporting to the AI Lead Fully Remote – Enjoy the flexibility of working from anywhere Top Equipment – State-of-the-art MacBook plus all the accessories you need ️ Wellbeing

Zur Position

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 development hands-on as a core technical contributor within a small, fast-moving team where engineers own problems end-to-end and ship quickly Cross-functional Collaboration: - Work closely with product, design, and engineering teams to integrate AI capabilities into real product workflows - Translate complex AI concepts into practical product features and communicate them clearly to both technical and non-technical stakeholders