Data & ML Platform Engineer

About the Role

Join a dynamic Industrial AI company in Munich focused on developing a shared infrastructure to enable efficient data processing, machine learning experimentation, model training, and deployment in production environments.

This position sits at the crossroads of Data Engineering, Machine Learning, and Platform Engineering, driving the creation of dependable, scalable platforms that support Data Scientists and ML Engineers in building and operating AI solutions effectively.

Responsibilities

• Design and implement the shared platform infrastructure for data workflows and ML lifecycle management.
• Collaborate to develop production-grade data pipelines and ML services.
• Support experimentation and operationalization of machine learning models.

Required Skills and Experience

• Over three years in roles related to Data Engineering, ML Platform Engineering, MLOps, or Data Infrastructure.
• Proficient in Python programming for data and machine learning tasks.
• Experience with Apache Spark and Apache Airflow for distributed data processing and workflow orchestration.
• Familiarity with MLflow to manage machine learning lifecycle.
• Competent in Kubernetes for container orchestration and AWS cloud services.
• Knowledge in Infrastructure as Code using Terraform.
• Demonstrated experience in building and maintaining production data pipelines or ML infrastructure.

Preferred Skills

• Containerization with Docker.
• Deep learning frameworks such as PyTorch or TensorFlow.
• Experience with Databricks, Kafka, Kubeflow, and Argo Workflows or Argo CD.
• Hands-on knowledge of CI/CD pipelines tailored for data and ML applications.
• Competence in model serving and inference infrastructure.
• Monitoring and observability using Prometheus, Grafana, and OpenTelemetry.
• Expertise in data versioning, lineage, feature stores, and data quality frameworks.
• Experience handling industrial sensor, IoT, or similar types of data.