Job Description

Job Description

Roles & Responsibilities:

- Hands-on experience with AWS Sage Maker, including training, deployment, and monitoring of machine learning models.
- Develop and maintain Python scripts using Sage Maker 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 Sage Maker models.
- Implement and manage Terraform infrastructure for Sage Maker and other AWS services, including module creation and state management.
- Build and maintain Git Lab CI/CD pipelines, integrating with Dev SecOps tools such as Snitch, Sonar Qube, and Veracode.
- Robust working knowledge of AWS services, including Sage Maker, EC2, ECS, EKS, ECR, Lambda, VPC, and IAM.




- Set up and maintain monitoring solutions using AWS Cloud Watch 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 Sage Maker.
- Proficiency with Python, Docker, Terraform, Git Lab CI/CD, and AWS ecosystem.
- Strong problem-solving, troubleshooting, and collaboration skills.

📌 MLOPS Engineer (Chennai)
🏢 RandomTrees
📍 Chennai