Senior MLOps Engineer
EUR 60K-85K (estimate) Senior-level Full Time
Tasks
- Apply CI/CD best practices
- Build automated ML pipelines
- Collaborate with data science and software engineering teams
- Deploy and serve models as production services
- Design ML deployment infrastructure
- Implement model registry observability security and governance
- Implement model versioning and reproducibility
- Monitor model performance and data drift
- Secure and operate ML systems with access control
- Track system health and production signals
- Train evaluate deploy and retrain models
Perks/Benefits
- Flexible location
- Hybrid work
- Private insurance
- Remote work
- Top-tier tech gear
- Training budget
- Work with experts
Skills/Tech-stack
AWS | Amazon SageMaker | Apache Kafka | Azure Machine Learning | BentoML | CI/CD | Cloud platform | DVC | Data Drift | Delta Lake | DevOps | Docker | Embeddings | Event Driven | Event-driven architecture | Feast | Feature Store | GPU Memory Optimization | GPU memory | Google Cloud | Google Cloud Platform | Google Vertex | Google Vertex AI | Grafana | Inference Server | Infrastructure as Code | Kubeflow | Kubernetes | Langsmith | MLOps | MLflow | Machine Learning | Memory Optimization | Microsoft Azure | Model Monitoring | Observability | Prometheus | Pulumi | Python | Quantization | RAG | Ragas | TGI | Tecton | Terraform | Torchserve | Triton Inference | Triton Inference Server | VLLM | Vector Databases | Vertex AI | “as-code”
Education
Roles
Engineer | MLOps Engineer | Senior MLOps | Senior MLOps Engineer
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