MLOps Engineer - Implementation

MLOps Engineer - Implementation

BMW Group

Munich, Germany

What awaits you?

• You build and maintain end-to-end ML pipelines using workflow orchestration tools: from data ingestion to distributed training, evaluation, model compilation, and deployment-ready artefacts;
• Furthermore, you engineer petabyte-scale data pipelines that consume domain datasets, transforming raw MDF4 (.mf4) and MCAP log files into training-ready formats;
• You build tooling for efficient parallel readers, signal extraction, synchronisation of multi-sensor streams, and integration with dataset management platforms for visual QA and curation;
• Also, you manage experiment tracking, hyperparameter tuning and model registry, enforcing reproducibility, lineage, and approval gates from experiment to production;
• You develop and maintain model compilation and optimisation pipelines targeting in-vehicle Qualcomm Snapdragon Ride chips and/or NVIDIA automotive SoCs;
• On top, you operate observability stacks, providing dashboards, data-drift alerts, pipeline SLOs, and log aggregation.

What should you bring along?

• University degree in Computer Science, Engineering, or a related field;
• 3–5 years of hands-on ML infrastructure or MLOps experience;
• Strong Python skills; experience with hermetic build systems (e.g., Bazel) is a plus;
• Production Kubernetes experience, including deploying and debugging workloads, writing Helm charts, and managing accelerator node pools;
• Working knowledge of ML pipeline orchestration, experiment tracking, and hyperparameter optimization;
• Hands-on experience with infrastructure-as-code for AWS (e.g., Terraform) and automotive measurement data, such as MDF4 or MCAP;
• Comfortable with relational databases (e.g., PostgreSQL) for metadata stores and experience with dataset management tools, functional-safety awareness (ISO 26262), or AUTOSAR Adaptive.