MLOps Engineer
Tasks
- Automate model deployment
- Build monitoring logging alerting observability solutions
- Build scalable data pipelines
- Design model-serving infrastructure
- Develop machine learning pipelines
- Enable autoscaling
- Execute rollback mechanisms
- Implement deployment strategies
- Implement storage architectures
- Improve platform scalability security governance
- Maintain CI/CD workflows
- Maintain feature stores
- Manage cloud resources
- Optimize GPU efficiency
- Optimize inference for low latency and high throughput
- Orchestrate model training and inference
- Validate production model releases
Perks/Benefits
- Fully remote Europe
- Healthcare coverage
- Paid parental leave
- Professional growth
- Relocation assistance
- Technical ownership
Skills/Tech-stack
A/B | A/B Testing | AWS | Alerting | Azure | B testing | Bash | CI/CD | Canary Releases | Databricks | Datadog | Docker | FastAPI | GCP | GitOps | Go | Grafana | Helm | High Throughput | Infrastructure as Code | KServe | Kubeflow | Kubernetes | Logging | Low Latency | MLOps | MLflow | Machine Learning | Model Serving | Monitoring | NVIDIA Triton | Observability | OpenTelemetry | Orchestration | Prometheus | PyTorch | Python | Snowflake | TensorFlow | TensorFlow Serving | Terraform | Torchserve | Weights and Biases | “as-code”
Education
N/A
Roles
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