Senior Data Engineer, AI

TeamViewer

full-time

Posted on: 9/2/2025

Location: 🇩🇪 Germany

✨ AI Apply

Senior

AirflowAWSAzureCloudDockerGoogle Cloud PlatformKubernetesPandasPythonReactSQL

About the role

Design, build, and optimize data pipelines and AI/ML infrastructure to support automation and AI projects across multiple domains
Develop and deploy LLM-driven applications and automation solutions, focusing on process automation, workflow orchestration, and enterprise integrations
Support data preparation, curation, and fine-tuning for large language models and ML workflows
Ensure reliability, scalability, and efficiency of AI applications within TeamViewer’s ecosystem
Collaborate with both internal and external stakeholders to identify automation opportunities and consult on AI/ML technologies
Participate in technology evaluations and feasibility studies for new AI/ML tools and frameworks
Promote best practices in data engineering, machine learning, and responsible AI usage
Be part of a newly established AI team consulting, building, and deploying automation and AI solutions for internal and external projects
Collaborate with other teams experimenting with LLMs and ML to align on technologies and best practices
Report to the AI Team Lead and create measurable business impact in an agile, modern environment

University degree in computer science, data engineering, machine learning, or a related field
3+ years of professional experience in data engineering or ML engineering
Strong experience with Python (pandas, SQLAlchemy, FastAPI, etc.) and data frameworks
Knowledge of vector databases (e.g., Pinecone, Weaviate, Milvus, pgvector)
Practical experience with LLMs (fine-tuning, RAG, prompt engineering, evaluation)
Understanding of LangChain / LangGraph (or willingness to learn)
Proficiency in SQL and experience working with relational databases and data warehouses
Familiarity with MCP (Model Context Protocol) and orchestration frameworks
Experience with machine learning pipelines and model lifecycle management
Familiarity with enterprise automation frameworks (e.g., Airflow, Prefect, Dagster) and integration platforms
Fluency in English is mandatory; German is a plus
Nice to Have: Hands-on experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes)
Nice to Have: Background in MLOps tools (MLflow, Weights & Biases, Kubeflow)
Nice to Have: Knowledge of data governance, security, and compliance for AI solutions
Nice to Have: Experience in front-end integration of AI tools (e.g., Streamlit, Gradio, or React for prototypes)