Description

You will design, deploy, and optimize cloud-native machine learning systems to power predictive-automation platforms.

Responsibilities

• Transform data science prototypes into secure, highly available AWS services using appropriate algorithms, container images, and instance types.
• Design and productionize cloud ML pipelines using SageMaker, Step Functions, and EKS.
• Integrate foundation models via Bedrock and Anthropic LLM APIs to enable 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 for online and offline inference.
• Extend or contribute to internal ML libraries, SDKs, and infrastructure-as-code modules via CDK or Terraform.

Required Skills

• Hands-on experience with AWS SDK, SageMaker, Lambda, and Step Functions.
• Proficiency in Python, Java, or Springboot, and JavaScript/TypeScript.
• Experience with DevOps and CI/CD tools including Docker, GitHub Actions, and Terraform or CDK.
• Strong understanding of machine learning theory, supervised learning, and deep learning.
• Practical knowledge of cloud security including IAM, KMS, VPC, and GuardDuty.
• Solid grasp of probability, statistics, algorithms, and data structures.
• Competency in API design using REST or GraphQL.
• Ability to manage Linux administration and scripting.
• Experience with Bedrock and Anthropic LLM integration.

Preferred Skills

• AWS Certified Machine Learning – Specialty or AWS Solutions Architect – Associate.
• Familiarity with PyTorch, TensorFlow, or Keras.

Key Skills

Education

Any Graduate