ML Ops Engineer
Tasks
- Build and orchestrate ML pipelines
- Create infrastructure as code for ML environments
- Deploy and operate ML models on AWS
- Detect data drift and prediction quality degradation
- Develop incident response runbooks
- Ensure HIPAA and SOC 2 compliance for ML data and systems
- Handle safe model rollouts and rollbacks
- Implement data and dataset versioning
- Implement pipeline monitoring and alerting
- Integrate Snowflake for feature generation
- Maintain audit trails for model decisions and data access
- Maintain operational dashboards
- Manage ML model versioning and registry
- Optimize ML compute and storage costs
- Track experiments and model metadata
Perks/Benefits
- N/A
Skills/Tech-stack
AWS Batch | AWS EC2 | AWS IAM | Alerting | Amazon CloudWatch | Amazon S3 | Apache Airflow | Audit Logging | CI/CD | Canary Releases | Containerization | Data Drift | Data Drift Detection | Data Versioning | Dataset Management | Docker | Drift Detection | Git | HIPAA | Health information | Infrastructure Monitoring | Infrastructure as Code | Logging | MLflow | Monitoring | Prediction Quality | Prediction Quality Monitoring | Protected Health Information | Python | Quality monitoring | SOC 2 | SQL | Shadow Deployments | Snowflake | “as-code”
Education
N/A
Regions
Countries
States
Cities
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