MLOps Engineer
Tasks
- Apply operational best practices
- Automate CI/CD workflows
- Build monitoring logging alerting
- Collaborate on platform scalability
- Conduct A/B testing
- Design feature stores
- Design model-serving infrastructure
- Develop machine learning pipelines
- Enable autoscaling
- Enhance model governance
- Ensure model reliability and performance
- Implement deployment strategies
- Implement rollback mechanisms
- Improve GPU efficiency
- Improve platform security
- Maintain scalable data pipelines
- Manage cloud resources
- Optimize inference latency and throughput
- Orchestrate model development and deployment
- Perform canary releases
- Validate production deployments
Perks/Benefits
- Fully remote within Europe
- Healthcare coverage
- Paid parental leave
- Professional growth
- Relocation assistance
- Technical ownership
Skills/Tech-stack
A/B | A/B Testing | AWS | Alerting | Autoscaling | Azure | B testing | Bash | CI/CD | Canary Releases | Cloud infrastructure | Databricks | Datadog | FastAPI | GCP | Git | GitOps | Go | Grafana | Helm | KServe | Kubeflow | Kubernetes | Logging | MLOps | MLflow | Machine Learning | Model Serving | Monitoring | NVIDIA Triton | Observability | OpenTelemetry | Prometheus | PyTorch | Python | Snowflake | TensorFlow | TensorFlow Serving | Terraform | Torchserve | Weights and Biases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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