Lead AI Engineer & Chapter Owner

Your role in the team

• We are steadily expanding the Data & AI Chapter and are looking for the person who will shape it professionally. As Lead AI Engineer & Chapter Owner, you will hold two roles simultaneously: You are technically proficient enough to contribute to complex projects and make architecture decisions. And you are broad enough to build a chapter that sets standards—in terms of quality, methodology, and impact.

• You are responsible for how we, as lab25, think, build, and develop AI systems. This is not purely a leadership role - you stay close to engineering. But you set the framework that enables others to perform at their best.

• You are responsible for the professional design and strategic orientation of the Data & AI Chapter: capability development, technology stack, standards, and quality level.

• You actively develop chapter members through substantive feedback, structured reviews, and clear development paths from Experienced to Senior.

• You are a sparring partner for other Chapter Leads and the management team in making strategic technological decisions.

• You are responsible for the Chapter knowledge base: agent patterns, pipeline templates, evaluation frameworks – what has been well designed once is made reusable.

• You design and review modular agent systems with clear roles, memory structures, tool use, and decision logic — from architectural decisions to production deployment.

• You set the standard for how we systematically design context windows, tool chains, and multi-stage inference pipelines – reproducible, versionable, and evaluable.

• You make well-informed architectural decisions regarding retrieval systems (chunking, embedding, re-ranking) and ensure that corporate knowledge is meaningfully usable for agents.

• You establish quality and safety standards within the chapter - bias tests, accuracy evaluation, guardrails - and make them a lived practice, not just a checklist.

• You develop and maintain the reusable agent patterns of the chapter.

• You assume overall technical responsibility in strategically relevant customer projects and serve as a peer contact for technical decision-makers at the customer.

• You actively drive research collaborations with universities and universities of applied sciences — from project conception to implementation — and ensure that insights are translated into practice.

What we offer

• An experienced, multidisciplinary team in which you work closely with designers and engineers, among others.

• Real design freedom: You build a chapter with - technically, culturally, strategically. Your decisions shape how lab25 AI systems think and implement.

• Direct impact: Direct line to management, quick decisions, no political detours. Your architectural decisions are implemented in real systems at relevant companies.

• Chapter structure: You lead a growing team of AI Engineers, ML Engineers, and Data Architects - with high standards and minimal bureaucracy.

• Working with modern technologies and a stable infrastructure.

• A modern workplace at the [whyit] campus and the opportunity to work partly from home.

• Competitive compensation including benefits as well as work equipment of your choice (MacOS or Linux).

Technologies and skills

• Python

• FastAPI

• Qdrant

• LangChain

• Neo4j

• CrewAI

• Weaviate

• LlamaIndex

• Docker

Our expectations:

Qualifications

• You have not only built agent systems and AI pipelines but also brought them into operational use, managed, and further developed them, and you know where the practical challenges truly lie.

• You have led teams or communities professionally—as a Tech Lead, Chapter Lead, Principal Engineer, or in a comparable role. You know how to enforce standards without slowing down.

• Deep Python knowledge, proficient handling of LLM frameworks (e.g., LangChain, LlamaIndex, CrewAI or similar), as well as vector and graph databases (e.g., Qdrant, Weaviate, Neo4j).

• You understand Context Engineering and AI Pipelines as a discipline at the system level and can transfer this understanding to others.

• You develop AI-enabled tools: Claude Code, Codex, and comparable tools are an integral part of your engineering workflow, and you expect the same from your chapter.

• You are fluent in German and English.

• You can make technical complexity understandable at decision-maker level and clearly justify architecture decisions.

Experience

• 8+ years of experience in software development or AI/ML with demonstrable, in-depth engagement with LLMs, agents, or RAG architectures, preferably also from the field of Applied Research, open-source projects, or the Databricks/Azure stack.

• Experience with deployment (FastAPI, Docker, Cloud) and MLOps fundamentals is required.