AWS SageMaker / Machine Learning Engineer

Job summary

Develop, train, deploy, and monitor machine learning models using AWS SageMaker. Implement MLOps pipelines and manage the machine learning lifecycle, including feature engineering and use of SageMaker Pipelines, Model Registry, and Feature Store.

Job details

Experience Required: 8+ years of overall IT experience Strong hands-on experience with AWS cloud services Experience implementing MLOps pipelines and ML lifecycle management Strong Python development skills Hands-on experience with SageMaker Pipelines, Model Registry, and Feature Store Experience with CI/CD tools and cloud-native development practices Top 3 Required Skills: AWS SageMaker (Model Development, Training, Deployment, Monitoring) AWS SageMaker Feature Store and Feature Engineering MLOps and Python Development Top 3 Preferred Skills: AWS Bedrock / Generative AI Terraform / CloudFormation Kubernetes and Docker

What you’ll do

Develop, train, deploy, and monitor machine learning models using AWS SageMaker. Implement MLOps pipelines and manage the machine learning lifecycle, including feature engineering and use of SageMaker Pipelines, Model Registry, and Feature Store.

Requirements

Requires 8 or more years of overall IT experience, strong hands-on experience with AWS cloud services, and strong Python development skills. Candidates should have experience implementing MLOps pipelines and ML lifecycle management, with preferred experience in AWS Bedrock or generative AI, Terraform or CloudFormation, and Kubernetes and Docker.

Listed skills

• Machine learning · Preferred
• Python · Preferred
• CI/CD · Preferred
• Terraform · Preferred
• Kubernetes · Preferred
• Docker · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• AWS SageMaker
• Machine Learning
• MLOps
• Python
• SageMaker Pipelines
• SageMaker Model Registry
• SageMaker Feature Store
• Feature Engineering
• CI/CD
• AWS Bedrock
• Generative AI
• Terraform
• CloudFormation
• Kubernetes
• Docker

Job areas

• Technology
• Software
• Data & Analytics
• Engineering

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