What you will work on

• Develop and manage scalable infrastructure and deployment workflows in AWS for data and machine learning applications
• Build cloud-native systems with focus on infrastructure as code, containerization, and CI/CD automation
• Author infrastructure using AWS CDK with strong proficiency in AWS services and Python
• Support ML workflows by integrating services like SageMaker and contributing to model operations infrastructure
• Infrastructure Development & Automation:
• Design, provision, and manage infrastructure in AWS using CDK and CloudFormation
• Build secure, scalable, and cost-effective environments for machine learning and analytics workloads
• Operate cloud-native services (e.g. EC2, ECS, Lambda, S3, RDS, SageMaker, Bedrock)
• Apply best practices for security, compliance, and disaster recovery
• CI/CD & Deployment Automation:
• Design and maintain deployment pipelines using CodePipeline, CodeBuild, GitHub Actions, or similar
• Automate testing, deployment, and rollback processes
• Containerization & Orchestration:
• Build and manage containerized applications using Docker
• Deploy services on ECS or Lambda with container-based runtimes
• Set up image build, versioning, and artifact management workflows
• Machine Learning & Model Operations Support:
• Collaborate with ML engineers to deploy and maintain models in SageMaker
• Integrate pipelines for pre-processing, inference, and model retraining
• Monitor model performance, logging, and metrics
• Monitoring, Observability & Logging:
• Set up alerting and observability tools (e.g. CloudWatch, DataDog)
• Investigate and resolve infrastructure, deployment, and performance issues
• Collaboration & Documentation:
• Partner with ML, software, and data teams to support DevOps practices
• Maintain documentation for infrastructure and operational workflows
• Participate in architecture discussions and code reviews