Description

You will design, build, and maintain scalable MLOps platforms on AWS, automating end-to-end machine learning environments for model development, training, deployment, and monitoring.

This role is on-site.

Responsibilities

• Develop and maintain CI/CD pipelines for application and ML model deployments using Jenkins, GitHub Actions, or GitLab CI.
• Build Infrastructure as Code using Terraform or CloudFormation to manage cloud resources.
• Deploy and manage containerized applications using Docker, Amazon ECS, and Kubernetes (EKS preferred).
• Integrate ML workflows with Amazon SageMaker and configure data integrations with Snowflake.
• Provide technical leadership, mentoring, and architectural guidance while ensuring security and compliance across AWS environments.

Required Skills

• 12+ years of overall IT experience with strong DevOps and MLOps implementation background.
• Deep expertise in Amazon Web Services (AWS) and hands-on experience with Amazon SageMaker.
• Strong understanding of Machine Learning Operations (MLOps) concepts and best practices.
• Experience with Infrastructure as Code (Terraform/CloudFormation) and CI/CD automation.
• Expertise in Docker, Kubernetes (EKS), and Amazon ECS.
• Experience working with Snowflake and supporting enterprise Data Science platforms.
• Strong scripting skills using Python, Bash, or similar languages.

Preferred Skills

• AWS Certifications (Solutions Architect, DevOps Engineer, or Machine Learning Specialty).
• Experience leading technical teams and driving cloud transformation initiatives.
• Knowledge of monitoring tools such as CloudWatch, Prometheus, or Grafana.

Key Skills

Education

Bachelor's degree