Description

Key Responsibilities

Build and maintain scalable ML infrastructure on AWS.

Automate model training, deployment, monitoring, and retraining workflows.

Develop CI/CD pipelines for machine learning applications.

Implement model performance monitoring, drift detection, and automated retraining.

Manage model registry, experiment tracking, and deployment governance.

Collaborate with Data Scientists and Platform Engineers to operationalize ML models.

Required Skills

AWS ML Services

Amazon SageMaker

Pipelines

Processing Jobs

Endpoints

Model Monitor

MLOps & Lifecycle Management

MLflow

Model Registry

Model Lifecycle Management

Automated Retraining

AWS Services

AWS Step Functions

Amazon EventBridge

AWS ECS Fargate

Amazon ECR

DevOps & Automation

Jenkins

AWS CodePipeline

CI/CD for Machine Learning

Infrastructure

Infrastructure as Code (CDK/CloudFormation)

Docker

Auto-scaling and Model Serving

Programming

Python

Experience

8+ years in MLOps, Cloud Engineering, or Machine Learning Platform Engineering.

Hands-on experience deploying and managing enterprise-scale ML platforms

Key Skills

Education

Bachelor's degree