Description

Key Responsibilities

• Design, build, and maintain scalable MLOps platforms on AWS.
• Automate end-to-end Machine Learning environments for model development, training, deployment, and monitoring.
• Develop and maintain CI/CD pipelines for application and ML model deployments.
• Build Infrastructure as Code (IaC) using tools such as Terraform or CloudFormation.
• Deploy and manage containerized applications using Docker, ECS, and Kubernetes (EKS preferred).
• Implement automation to support Data Science and Machine Learning initiatives.
• Integrate ML workflows with Amazon SageMaker for model training and deployment.
• Configure and manage data integrations with Snowflake.
• Monitor cloud infrastructure, optimize performance, and ensure high availability.
• Collaborate with Data Scientists, ML Engineers, and Development teams to streamline ML lifecycle management.
• Ensure security, compliance, and best practices across AWS environments.
• Provide technical leadership, mentoring, and architectural guidance to the team.

Required Skills

• 12+ years of overall IT experience.
• Strong experience as a DevOps Engineer with hands-on MLOps implementation.
• Deep expertise in Amazon Web Services (AWS).
• Hands-on experience with Amazon SageMaker.
• Strong understanding of Machine Learning Operations (MLOps) concepts and best practices.
• Experience with Infrastructure as Code (Terraform/CloudFormation).
• Expertise in CI/CD automation (Jenkins, GitHub Actions, GitLab CI, Azure DevOps, etc.).
• Strong experience with Docker, Kubernetes (EKS), and Amazon ECS.
• Experience working with Snowflake.
• Experience supporting enterprise Data Science platforms and ML pipelines.
• Strong scripting skills using Python, Bash, or similar languages.
• Excellent troubleshooting and problem-solving skills.

Preferred Skills

• Experience with ML model lifecycle management and model governance.
• Knowledge of monitoring tools such as CloudWatch, Prometheus, or Grafana.
• Experience with security best practices in AWS.
• AWS Certifications (Solutions Architect, DevOps Engineer, or Machine Learning Specialty) are a plus.
• Experience leading technical teams and driving cloud transformation initiatives.

Mandatory Skills

• AWS
• MLOps
• Amazon SageMaker
• CI/CD Automation
• Infrastructure as Code (Terraform/CloudFormation)
• Kubernetes (EKS)
• Amazon ECS
• Snowflake
• Docker
• Python/Bash
• DevOps Leadership

Key Skills

Education

Bachelor's degree