ML Ops Engineer
Tasks
- Build MLflow experiment tracking and model registry workflows
- Build infrastructure as code for ML environments
- Build model retraining pipelines
- Create incident response procedures and runbooks
- Deploy and operate ML models on AWS
- Develop operational dashboards
- Document ML platform operational processes and architecture decisions
- Ensure ML reproducibility and experiment tracking
- Ensure healthcare security and privacy compliance
- Establish experiment logging and artifact storage conventions
- Implement CI CD for models and automated testing for pipelines
- Implement pipeline monitoring alerting and failure recovery
- Implement production model performance monitoring
- Implement safe model rollout strategies
- Integrate Snowflake for feature generation and training pipelines
- Maintain audit trails for model decisions and data access
- Maintain training data versioning and dataset management
- Manage AWS compute resources for ML workloads
- Manage model versioning promotion and rollback
- Optimize ML compute storage and data transfer cost
- Own ML pipeline orchestration
- Support model deployment to edge devices
- Track dataset lineage labeling provenance and feature dependencies
Perks/Benefits
- N/A
Skills/Tech-stack
AWS Batch | AWS IAM | Alerting | Amazon CloudWatch | Amazon EC2 | Amazon S3 | Apache Airflow | Apache Spark | CI/CD | Containerization | DVC | Dask | Data Drift | Data Drift Detection | Data Versioning | Dataset Lineage | Docker | Drift Detection | Feature Lineage | Git | HIPAA | Incident Response | Infrastructure as Code | LakeFS | Linux | Logging | MLflow | Monitoring | Python | Real Time | Real-time Inference | SOC 2 | SQL | Snowflake | Streaming | “as-code”
Education
N/A
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