MLOps Lead
Tasks
- Build automated training pipelines
- Collaborate with research teams for model productionization
- Define engineering standards and operational best practices
- Define execute MLOps roadmap
- Deploy and scale model serving platforms
- Design implement maintain machine learning infrastructure
- Implement CI/CD workflows
- Implement monitoring, logging, alerting, observability
- Implement orchestration frameworks
- Lead mentor develop MLOps team
- Optimize feature stores and data pipelines
Perks/Benefits
- Continuous learning culture
- Fully remote work
- Healthcare coverage
- International collaboration
- Paid parental leave
- Relocation assistance
- Technical ownership
Skills/Tech-stack
AWS | Azure | Bash | Databricks | Datadog | FastAPI | GCP | 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 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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