AWS AI ML Engineers

Deployment and Operations:
Responsible for the continuous deployment, integration, and monitoring of ML models.
Use AWS DevOps and MLOps tools to manage workflows and maintain uptime.
Automation and CI/CD:
Implement CI/CD pipelines for model deployment and integration with data pipelines.
Automate testing, validation, and retraining of models to support scalability.
Infrastructure Management:
Manage underlying AWS infrastructure (EC2, S3, Lambda, etc.) required for model hosting.
Optimize resource allocation to minimize costs while meeting performance needs.
Data Pipeline Maintenance:
Support and maintain data ingestion, transformation, and feature engineering pipelines.
Ensure seamless integration with production workflows and data updates.
Monitoring and Logging:
Implement monitoring solutions to track model performance and health.
Maintain logs, set up alerts, and work on troubleshooting any issues.
Security and Compliance (Operational Focus):
Implement and enforce security policies for data and model access.
Ensure compliance from an operational standpoint (e.g., permissions, audit logs).
Collaboration and Support:
Work closely with data scientists to facilitate model handovers.
Provide support in resolving issues with model deployment and performance.
Continuous Improvement:
Identify opportunities for process improvements and automation.
Experiment with new MLOps tools and methods to enhance pipeline efficiency.