Description

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

Responsibilities

• Transform data science prototypes into secure, highly available AWS services using appropriate algorithms, container images, and instance types.
• Productionize cloud ML pipelines using SageMaker, Step Functions, and EKS.
• Integrate foundation models via Bedrock and Anthropic LLM APIs for generative AI capabilities.
• Train, retrain, and monitor models using SageMaker Pipelines, Model Registry, and CloudWatch alarms.
• Build and maintain optimized data pipelines using Glue, Kinesis, Athena, and Iceberg.
• Extend or contribute to internal ML libraries, SDKs, and infrastructure-as-code modules using CDK or Terraform.

Required Skills

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

Key Skills

Education

Any Gradute