Senior MLOps Engineer
Tasks
- Automate ML pipeline infrastructure with infrastructure as code
- Define and automate monitoring alerting and retraining for deployed models
- Design build and maintain ML Ops pipelines
- Develop integrations between data ingestion feature stores and model repositories
- Ensure ML pipelines meet security compliance and governance standards
- Implement automation for model packaging testing deployment monitoring
- Manage model versioning reproducibility and lineage tracking
- Mentor junior engineers and maintain documentation and standards
- Operationalize ML workloads in data lakehouse ecosystem
- Participate in code reviews and knowledge sharing
Perks/Benefits
Skills/Tech-stack
AWS CDK | AWS Glue | AWS IAM | AWS Lambda | AWS SageMaker | AWS Step Functions | Amazon ECR | Amazon S3 | Apache Iceberg | CI/CD | CloudFormation | CloudWatch | Containerization | Data Catalogs | Data Lakehouse | Delta Lake | Docker | Drift Detection | ELT | ETL | Feature Engineering | GitHub Actions | Infrastructure as Code | Jenkins | MLflow | Metadata Management | Model Drift | Model Drift Detection | Model Governance | Model Monitoring | Model Registry | Model lineage | PyTorch | Python | SageMaker Model Registry | Scikit-learn | Step Functions | TensorFlow | Terraform | “as-code”
Education
Roles
Engineer | ML Ops Engineer | MLOps Engineer | Ops Engineer | Senior MLOps | Senior MLOps Engineer
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