AI Engineer

Role overview Build applied AI systems that improve operational workflows, knowledge access, and decision-making. The work spans machine learning, generative AI, agent-based applications, and federated learning, from preparing data and building prototypes through integrating and monitoring production solutions.

Responsibilities - Develop AI applications and Python pipelines using LLM and machine learning frameworks. - Build text and image retrieval-augmented generation systems and internal chatbots. - Create automations for tasks such as document analysis, report generation, internal search, and decision support. - Integrate and scale solutions with cross-functional teams, translating business needs into practical tools. - Track model quality and performance, applying MLOps practices to production systems. - Prepare data pipelines and train machine learning models.

Requirements - Bachelor’s or master’s degree in computer science, data science, artificial intelligence, or a related field. - Strong Python skills and hands-on experience with AI or LLM frameworks such as CrewAI, LangChain, Transformers, or vLLM. - Understanding of RAG architectures and vector storage, such as FAISS, Chroma, or PostgreSQL with pgvector. - Experience building APIs, for example with FastAPI. - Practical MLOps knowledge, including Docker, CI/CD, model versioning, and observability. - Analytical problem-solving, clear communication, and a collaborative approach.

Nice to have - Further training or certifications in machine learning, deep learning, or MLOps. - Experience with coding agents, context engineering, or LLM fine-tuning. - Familiarity connecting AI tools to internal platforms such as chat, knowledge, or CRM systems. - Interest in agentic systems and product-focused applied AI.

Benefits and work setup - Annual salary of €27,000–€35,000, depending on experience and technical level. - Health insurance and a meal allowance.