MLOps Lead
Tasks
- Build automated training pipelines
- Define MLOps roadmap
- Design machine learning infrastructure
- Drive model serving architecture
- Ensure model performance and drift detection
- Establish engineering standards and best practices
- Implement CI/CD workflows
- Implement monitoring, logging, alerting
- Integrate research to production
- Lead MLOps team
- Optimize feature stores and data pipelines
- Orchestrate model deployment pipelines
Perks/Benefits
- Continuous learning
- Fully remote work
- Healthcare coverage
- Inclusive culture
- International collaboration opportunities
- Paid parental leave
- Relocation assistance
- Technical ownership
Skills/Tech-stack
AWS | Azure | Bash | CI/CD | Data pipeline | Databricks | Datadog | FastAPI | Feature Store | 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 and Biases
Education
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