Description

You design, deploy, and optimize cloud-native machine learning systems on AWS.

10+ years of professional experience required. Degree in any graduate field.

Responsibilities

• Transform data science prototypes into secure, highly available AWS services.
• Productionize cloud ML pipelines using SageMaker, Step Functions, and EKS.
• Build and maintain optimized data pipelines using Glue, Kinesis, Athena, and Iceberg.
• Train, retrain, and monitor models using SageMaker Pipelines and CloudWatch alarms.
• Extend internal ML libraries and infrastructure-as-code modules.

Required Skills

• 10+ years of professional software or ML engineering experience.
• Deep expertise in AWS and MLOps practices.
• Hands-on experience with SageMaker, Step Functions, and EKS.
• Proficiency with Bedrock and Anthropic LLM APIs for generative AI.
• Experience with Glue, Kinesis, Athena, and Iceberg for data pipelines.
• Ability to use CDK or Terraform for infrastructure-as-code.
• Experience monitoring systems with CloudWatch.
• Knowledge of detecting and mitigating data-distribution drift.

Key Skills

Education

Any Gradute