MLOps Lead
Tasks
- Build and optimize feature stores
- Build automated training pipelines
- Collaborate with research teams to productionize models
- Define and execute MLOps roadmap
- Define engineering standards and operational best practices
- Deploy and scale low latency inference services
- Design implement and maintain machine learning infrastructure
- Detect model drift and operational issues
- Drive model serving platform architecture
- Establish data pipelines and storage solutions
- Implement CI/CD workflows
- Implement monitoring logging alerting and observability
- Lead and mentor MLops engineers
- Orchestrate model training and deployment
Perks/Benefits
- 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 | GitOps | Go | Grafana | Helm | Inference Server | Infrastructure as Code | KServe | Kubernetes | MLflow | Model Deployment | Model Monitoring | Model Serving | OpenAI | OpenTelemetry | Prometheus | PyTorch | Python | Snowflake | TensorFlow | TensorFlow Serving | Terraform | Torchserve | Triton Inference | Triton Inference Server | Weights and Biases | “as-code”
Education
Roles
Engineer | Lead | Learning Engineer | MLOps Lead | Machine Learning Engineer
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