Description

You own the management and support of the MLOps platform components across AWS and EKS-based environments.

This role is hybrid.

Responsibilities

• Oversee deployment and operation of infrastructure for ML training, batch inference, and real-time model serving.
• Build and support CI/CD pipelines in GitLab for model packaging, container builds, vulnerability scanning, and automated deployments.
• Design and manage EKS workloads for containerized ML jobs and microservices, implementing auto-scaling and resource quotas.
• Implement logging, monitoring, and alerting across ML pipelines and perform incident response.
• Partner with Data Scientists and ML Engineers to operationalize end-to-end machine learning solutions.

Required Skills

• 3+ years of hands-on experience with AWS services including EKS, EC2, S3, IAM, CloudWatch, and ECR.
• Strong experience operating and troubleshooting Kubernetes, preferably AWS EKS.
• Proficiency in containerization using Docker and orchestration concepts.
• Strong programming and scripting experience in Python and Bash.
• Experience building and managing CI/CD pipelines using GitLab or equivalent.
• Experience with infrastructure-as-code using Terraform or CloudFormation.
• Familiarity with machine learning workflows, including training, inference, and model monitoring.
• Experience supporting production platforms, including incident management and root cause analysis.

Preferred Skills

• 5+ years of total working experience in platform engineering or DevOps roles.
• Experience contributing to and maintaining runbooks for production systems.

Key Skills

Education

Any Graduate