- 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 environments.
- Apply strong 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.