AI Engineer

Join our team for innovative and stimulating GenAI projects. You will benefit from a collaborative and enriching work environment.

Tasks ##

Design and implement LLM-based applications.
Create RAG systems and extract structured information.
Evaluate solutions according to the EDD principle and collaborate with the team.

Skills ##

What awaits you

Exciting and innovative GenAI projects for our clients: from creative workshops to proofs of concept to the joint implementation of productive solutions
Design and implementation of LLM-based applications
Design and implementation of retrieval-augmented generation (RAG) systems
Implementation of structured information extraction from unstructured documents
Design and realization of agent systems and AI workflows
Prompt and context engineering for targeted control of LLM-based solutions
Rigorous evaluation of your solutions according to the evaluation-driven development (EDD) principle, including observability via AI evaluation and observability platforms
You will also bring your expertise during the bidding phase and contribute technically to the drafting of proposals
Optional contact points with classical software engineering and MLOps: when it makes sense, you will work closely with our software and ML engineers
Continuous learning and permanent training in the field of GenAI as well as insights into our clients' different sectors
Variety and exciting collaboration within internal project teams as well as on-site at our clients

What you bring

Several years of experience in GenAI application development, ideally in production for clients or own products
A bachelor's degree in computer science, data science, or a related field
Proven expertise in the implementation and operation of retrieval-augmented generation (RAG) systems
Experience with agent systems and AI workflows as well as the Model Context Protocol (MCP)
Rigorous management of LLM solution evaluation, familiarity with evaluation-driven development (EDD) and AI evaluation and observability platforms (AEOPs)
Knowledge of modern vector databases, e.g. Qdrant, as well as embedding and re-ranking models
Understanding of open-weight large language models, including multimodal models
Experience with LLM runtime environments as well as LLM proxies or gateways
Proficiency in Python and software engineering best practices
Excellent communication skills and pleasure in collaborating with the team and clients
Professional proficiency in German and English, both spoken and written
Asset: cloud experience in Infrastructure as Code (IaC), ideally Azure, as well as experience in guardrails for LLM applications

We are looking for you - join our team

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Posted 1 week ago

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