Senior MLOps Engineer
Tasks
- Architect and deploy ML models across full lifecycle
- Build CI/CD workflows for models
- Collaborate on operationalizing ML models in production
- Design retraining pipelines and rollback mechanisms
- Establish model registry and metadata standards
- Implement monitoring for data/model drift
- Optimize inference for cost and latency
Perks/Benefits
- N/A
Skills/Tech-stack
AWS SageMaker | Airflow | Azure ML | DVC | Dagster | Databricks | Docker | FastAPI | Flask | GCP Vertex | GCP Vertex AI | GitHub Actions | Grafana | Kubeflow | Kubernetes | MLflow | Prefect | Prometheus | PySpark | Python | SQL | Snowflake | Vertex AI | Weights & Biases
Education
Roles
Engineer | MLOps Engineer | Senior MLOps | Senior MLOps Engineer
Regions
Countries
States
Cities
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