Roles & Responsibilities:
Hands-on experience with AWS SageMaker, including training, deployment, and monitoring of machine learning models.
Develop and maintain Python scripts using SageMaker Python SDK and work efficiently in Linux settings.
Apply robust understanding of ML concepts and model lifecycle management.
Use Docker to build and manage containerized SageMaker models.
Implement and manage Terraform infrastructure for SageMaker and other AWS services, including module creation and state management.
Build and maintain GitLab CI/CD pipelines, integrating with DevSecOps tools such as Snitch, SonarQube, and Veracode.
Strong working knowledge of AWS services, including SageMaker, EC2, ECS, EKS, ECR, Lambda, VPC, and IAM.
Set up and maintain monitoring solutions using AWS CloudWatch to track model and infrastructure performance.
Collaborate effectively with team members and stakeholders, demonstrating strong communication and interpersonal skills.
Troubleshoot and resolve issues with a logical and pragmatic approach.
Preferred Candidate Profile:
Experience in ML model deployment and lifecycle management on AWS SageMaker.
Proficiency with Python, Docker, Terraform, GitLab CI/CD, and AWS ecosystem.
Robust problem-solving, troubleshooting, and collaboration skills.