MLOps Lead
Tasks
- Build data pipelines
- Build feature stores
- Collaborate with research teams
- Define MLOps roadmap
- Deploy and scale model serving platforms
- Design machine learning infrastructure
- Establish monitoring, logging, alerting
- Implement CI/CD workflows
- Implement observability for model drift detection
- Implement training pipelines
- Lead MLOps engineering team
- Orchestrate model deployments
- Serve ML models for low latency inference
- Set engineering standards and operational best practices
Perks/Benefits
- Continuous learning culture
- Healthcare coverage
- International collaboration opportunities
- Paid parental leave
- Relocation assistance
- Remote work
- Technical leadership opportunities
Skills/Tech-stack
AWS | Bash | CI/CD | Databricks | Datadog | FastAPI | Git | 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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