MLOps Lead
Tasks
- Build automated training pipelines
- Build feature stores
- Collaborate with research teams
- Define MLOps roadmap
- Define engineering standards
- Deploy machine learning models
- Design machine learning infrastructure
- Develop data pipelines
- Establish monitoring, logging, alerting
- Implement CI/CD workflows
- Implement observability with traces metrics logs
- Lead MLOps engineering team
- Orchestrate model workflows
- Serve low latency high throughput inference
Perks/Benefits
- Fully remote
- Healthcare coverage
- International collaboration opportunities
- Paid parental leave
- Relocation assistance
- Technical ownership
Skills/Tech-stack
AWS | Azure | Bash | Databricks | Datadog | FastAPI | GCP | Git | GitOps | Go | Grafana | Helm | Inference Server | KServe | Kubernetes | MLflow | OpenTelemetry | Prometheus | PyTorch | Python | Snowflake | TensorFlow | TensorFlow Serving | Terraform | Torchserve | Triton Inference | Triton Inference Server | Weights & Biases
Education
Roles
Engineer | Lead | Learning Engineer | MLOps Lead | Machine Learning Engineer
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