Description

Engineer and operate the MLOps platform on AWS EKS, supporting ML training, batch inference, and real-time model serving.

Responsibilities

• Build and maintain CI/CD pipelines in GitLab for model packaging, container image builds, vulnerability scanning, and automated deployments.
• Design and manage EKS workloads, implementing auto-scaling, resource quotas, multi-tenant isolation, and RBAC.
• Implement logging, monitoring, and alerting for ML pipelines, model endpoints, and platform components to ensure availability and performance.
• Optimize compute, storage, and data transfer costs across ML workloads through infrastructure tuning and resource analysis.
• Partner with Data Scientists and ML Engineers to operationalize end-to-end machine learning solutions and provide technical guidance.

Required Skills

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

Preferred Skills

• Experience managing Data Analytics Platforms such as Domino or SageMaker.
• Experience with ML lifecycle tools like MLflow.
• Experience supporting GPU-based workloads or distributed training environments.

Key Skills

Education

Any Gradute