MLOps Engineer - Implementation (f/m/x)
Tasks
- Build and maintain end-to-end ML pipelines
- Build and maintain model compilation and optimization pipelines
- Deploy and debug production workloads on Kubernetes
- Develop tooling for parallel data reading and signal extraction
- Engineer data pipelines for training datasets
- Maintain model registry for reproducibility and lineage
- Manage experiment tracking and hyperparameter tuning
- Operate observability stacks for dashboards and alerts
Perks/Benefits
- Discounted BMW and MINI conditions
- Flexible working hours
- High job security
- Overtime compensation
- Professional development opportunities
- Six weeks annual leave
Skills/Tech-stack
AUTOSAR | AWS | Bazel | Data Drift | Data Drift Detection | Data Lineage | Distributed Training | Drift Detection | Experiment tracking | Helm | Hyperparameter Optimization | ISO 26262 | Infrastructure as Code | Kubernetes | Log Aggregation | Machine Learning | Model Optimization | Model Registry | Model compilation | Observability | PostgreSQL | Python | Terraform | Workflow Orchestration | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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