onsite
ML Ops Engineer - Implementation - BMW AG
MLOps Engineer
Lead the deployment and scaling of machine learning models using Python, Docker, Kubernetes, and AWS, ensuring robust CI/CD pipelines and model governance with MLflow.
About the role
Key Responsibilities
- Design, build, and maintain end‑to‑end ML pipelines from data ingestion to model serving.
- Containerize models with Docker and orchestrate deployments on Kubernetes clusters.
- Implement CI/CD workflows for automated testing, packaging, and release of ML artifacts.
- Integrate model monitoring, logging, and alerting using AWS services and open‑source tools.
- Collaborate with data scientists, software engineers, and DevOps to ensure reproducibility and scalability.
Requirements
- Strong experience with Python and ML libraries (scikit‑learn, TensorFlow, PyTorch).
- Proven track record deploying ML models in production using Docker, Kubernetes, and AWS.
- Hands‑on knowledge of CI/CD tools (GitLab CI, Jenkins, ArgoCD) and version control.
- Familiarity with MLflow or similar model management platforms.
- Excellent problem‑solving skills and ability to work in a fast‑paced, cross‑functional team.
Skills
pythondockerkubernetesawsmlflowcicd