Design and implement modern Data & AI platforms based on Databricks
Develop robust and high-performance batch and streaming pipelines using Python, SQL, Spark and PySpark
Work on scalable Lakehouse architectures for analytics, ML and GenAI use cases
Utilize core Databricks components such as Delta Lake, Unity Catalog, Workflows, MLflow, Model Serving and Vector Search in production environments
Support the development and operationalization of ML, LLM and GenAI use cases
Work on topics such as serving, retrieval, evaluation, monitoring and governance for modern AI applications
Integrate various source systems such as APIs, databases, ERP or CRM systems
Implement technical requirements related to data quality, security, governance and observability
Automate deployments and technical standards with Git, CI/CD and Infrastructure as Code
Advise clients and internal stakeholders on architecture decisions for data platforms and AI solutions
Share knowledge within the team, provide technical guidance and support to less experienced colleagues

Several years of experience in Data Engineering, Machine Learning Engineering, AI Engineering or a comparable role
Strong knowledge of Databricks
Strong skills in Python, SQL, Spark and PySpark
Practical experience with at least one cloud platform, ideally Azure or AWS
Experience building and operating modern data platforms in the cloud
Experience with MLflow, Model Serving and Vector Search, ideally within the Databricks ecosystem
Good knowledge of modern AI/LLM architectures, e.g., RAG, retrieval, evaluation, serving and production GenAI applications
Solid understanding of Lakehouse architectures, data modeling and production data pipelines
Experience in Data Governance, Data Quality, Security and Monitoring
Confident use of Git, CI/CD and, ideally, Infrastructure as Code
Degree in Business Informatics, Computer Science, Data Science or a related field
Structured, self-driven and solution-oriented way of working

Senior Cloud Data & AI Engineer – Databricks

Key skills