MLOps Lead
Tasks
- Build automated training pipelines
- Collaborate with research to deploy models
- Define and execute MLOps roadmap
- Define engineering standards and operational best practices
- Design implement and maintain machine learning infrastructure
- Develop feature stores and data pipelines
- Drive model serving architecture decisions
- Ensure model performance and drift detection
- Implement CI/CD workflows
- Lead and mentor MLops engineers
- Orchestrate deployments and model releases
- Set up monitoring logging alerting observability
Perks/Benefits
- Continuous learning culture
- Fully remote
- Healthcare coverage
- International collaboration opportunities
- Paid parental leave
- Relocation assistance
- Technical ownership
Skills/Tech-stack
AWS | Bash | CI/CD | Databricks | Datadog | Docker | FastAPI | GitOps | Go | Google Cloud | Grafana | Helm | Inference Server | KServe | Kubernetes | MLflow | Microsoft Azure | OpenAPI | OpenTelemetry | Prometheus | PyTorch | Python | Snowflake | TensorFlow | TensorFlow Serving | Terraform | Torchserve | Triton Inference | Triton Inference Server | Weights and Biases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
Engineer | Lead | Learning Engineer | MLOps Lead | Machine Learning Engineer
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