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