MLOps Lead
Tasks
- Build data pipelines
- Build feature stores
- Build machine learning infrastructure
- Collaborate with research teams
- Define MLOps roadmap
- Deploy and scale machine learning models
- Design model serving architectures
- Establish engineering standards
- Establish monitoring, logging, alerting
- Implement CI/CD workflows
- Implement automated training pipelines
- Implement observability for model drift detection
- Lead MLOps team
- Optimize inference performance
Perks/Benefits
- Continuous learning
- Fully remote
- Healthcare coverage
- Inclusive culture
- International collaboration
- Paid parental leave
- Relocation assistance
- Technical ownership
Skills/Tech-stack
AWS | Alerting | Azure | Bash | CI/CD | Databricks | Datadog | FastAPI | Feature Engineering | Feature Store | GCP | GitOps | Go | Grafana | Helm | Inference Server | KServe | Kubernetes | Logging | MLflow | Machine Learning | Model Deployment | Model Monitoring | Observability | OpenAI Gym | OpenTelemetry | Prometheus | PyTorch | Python | Snowflake | TensorFlow | TensorFlow Serving | Terraform | Torchserve | Triton Inference | Triton Inference Server | Weights and Biases
Roles
Engineer | Lead | Learning Engineer | ML Engineer | MLOps Lead | Machine Learning Engineer
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