Description

You will design and build scalable ML and data systems within the AWS cloud environment.

Responsibilities

• Develop and maintain high-performance, distributed data pipelines and ML infrastructure.
• Convert machine learning models into production-ready services and APIs.
• Automate model training, validation, deployment, and inferencing pipelines.
• Design fault-tolerant, secure, and cost-effective cloud-native architectures using AWS services.
• Implement Infrastructure as Code (IaC) using CloudFormation, Terraform, or AWS CDK.

Required Skills

• 5+ years of professional experience developing backend systems using Python.
• Strong background building and optimizing large-scale data pipelines and distributed systems.
• Experience deploying ML models and managing ML workflows in production.
• Solid understanding of software engineering principles, including design patterns and CI/CD.
• Proven experience with cloud engineering on AWS, specifically SageMaker, Glue, Lake Formation, and Athena.
• Experience implementing IaC using CloudFormation, Terraform, or AWS CDK.
• Experience developing RESTful APIs and microservices.

Key Skills

Education

Any Graduate