MLOPS Engineer

Job Description 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 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. - Strong problem-solving, troubleshooting, and collaboration skills. Skills: Docker, Linux, Python Experience: 4.00-7.00 Years