AWS AI ML Engineers

Job Description

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.