MLOps Engineer
Tasks
- Automate CI/CD workflows
- Build scalable data pipelines
- Design model-serving infrastructure
- Develop machine learning pipelines
- Implement deployment strategies
- Improve platform scalability security governance
- Maintain feature stores
- Manage cloud resources with infrastructure as code
- Optimize GPU efficiency and autoscaling
- Optimize inference latency and throughput
- Orchestrate model development and deployment
- Set up monitoring logging alerting observability
Perks/Benefits
- Equity package
- Fully remote work within Europe
- Healthcare coverage
- Paid parental leave
- Relocation assistance
Skills/Tech-stack
AWS | Autoscaling | Azure | Bash | CI/CD | Data pipeline | Databricks | Datadog | FastAPI | Feature Store | GCP | GPU Optimization | GitOps | Go | Grafana | Helm | Inference Server | KServe | Kubeflow | Kubernetes | MLOps | MLflow | Machine Learning | OpenTelemetry | Prometheus | PyTorch | Python | Snowflake | TensorFlow | TensorFlow Serving | Terraform | Torchserve | Triton Inference | Triton Inference Server | Weights & Biases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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