Senior Data Engineer (Agentic AI) | f/m/d

ERGO Technology & Services S.A. (ET&S), a member of the Munich Re and ERGO Group, delivers integrated IT and business services to international markets. The company focuses on advanced IT services and business-driven technology solutions, supporting end-to-end insurance processes including finance, operations, and underwriting. It operates offices in Warsaw and Gdańsk and fosters a multicultural environment with global partnerships.

• Fluency in English and German
• Completed degree in computer science, artificial intelligence, or data
• Sound knowledge of GenAI, machine learning, deep learning, relevant algorithms and models, and RAG
• Experience in preparing, cleansing, and enriching data for AI applications
• Very good knowledge of one or more programming languages (preferably Python), especially AI frameworks like PyTorch
• Knowledge of database programming languages such as SQL
• Experience with tools, frameworks, and technologies for training, deploying, and monitoring AI models, including MLOps practices
• Familiarity with distributed systems and cloud platforms for scaling AI applications (e.g., Azure, Databricks, Kubernetes)
• Strong ability to identify and analyse data patterns and develop predictive models
• Ability to collaborate in interdisciplinary teams and communicate complex technical concepts clearly
• Strong service orientation, teamwork skills, and commitment to meeting business needs and advancing AI solutions
• Experience working in teams using git, creating pull requests, and performing code reviews

• Preparing and analysing data from various sources to develop and optimise Agentic AI and machine learning models
• Designing and standardising effective, scalable AI environments, AI blueprints, and processes
• Designing, implementing, and operating software solutions for structured and unstructured data, particularly in Agentic AI and machine learning
• Developing technical know-how, tools, and methods for Agentic AI
• Supporting operational units in implementing AI models into existing processes and systems
• Designing and piloting AI solutions for monitoring and evaluating outcomes of implemented use cases to measure AI effectiveness