TeamViewer

Senior Data Engineer, AI

TeamViewer

full-time

Posted on:

Origin:  • 🇩🇪 Germany

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Job Level

Senior

Tech Stack

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

Requirements

  • 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)