MLOps Engineer
Tasks
- Automate machine learning deployment
- Build observability solutions
- Build scalable data pipelines
- Collaborate on platform scalability and security
- Configure monitoring logging alerting
- Design feature stores
- Design model-serving infrastructure
- Develop machine learning pipelines
- Enable autoscaling
- Implement deployment strategies
- Improve GPU efficiency
- Manage cloud resources
- Optimize inference latency and throughput
- Orchestrate model development and deployment
- Run A B testing and canary releases
Perks/Benefits
- Fully remote within Europe
- Healthcare coverage
- Paid parental leave
- Professional growth
- Relocation assistance
- Technical ownership
Skills/Tech-stack
AWS | Autoscaling | Azure | Bash | CI/CD | Databricks | Datadog | Datapipelines | FastAPI | Feature Store | GitOps | Go | Google Cloud | Grafana | Helm | Infrastructure as Code | KServe | Kubeflow | Kubernetes | MLflow | Model Governance | Model Monitoring | NVIDIA Triton | Observability | OpenTelemetry | Prometheus | PyTorch | Python | SRE | Snowflake | TensorFlow | TensorFlow Serving | Terraform | Torchserve | Weights and Biases | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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