Level

JUPUS

Senior Applied AI Engineer

JUPUS is hiring a Senior Applied AI Engineer for a remote role open to applicants in Germany. Level rates it ; you can apply on Level.

AI in this role

ragai-agents

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.

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. 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 that automate workflows and perform multi-step tasks within the JUPUS platform

  • 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

Requirements

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

Other requirements:

  • You must be located in the CET -2/+2 zone for this role!

  • You must have at least 4 years of professional experience in AI

  • You must have deployed AI systems such as RAG pipelines or agentic systems into production

Benefits

🚀 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 Perks – Urban Sports Club membership to support your healthy lifestyle

If you're passionate about building practical AI systems, shipping agentic workflows, and creating real impact for users through generative AI, we’d love to hear from you.

How we rate this

Senior Applied AI Engineer at JUPUS rates 95 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.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ 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

RAGAI agents

Also common in AI engineer jobs at this level

Pay

Typical AI engineer job
$181k

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

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. How do you decide when an AI agent can act on its own versus asking for approval first?
  3. How would you decide a model or AI system is ready to ship?
  4. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: RAG and AI agents. 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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