Description

You build scalable ML and data systems by collaborating with Data Scientists and Platform Engineers.

Responsibilities

• Develop and maintain high-performance, distributed data pipelines and ML infrastructure.
• Convert ML 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 using CloudFormation, Terraform, or AWS CDK.

Required Skills

• 5+ years of professional experience developing backend systems using Python.
• Strong background in building and optimizing large-scale data pipelines and distributed systems.
• Experience deploying machine learning models and managing ML workflows in production.
• Solid understanding of software engineering principles, including design patterns, version control, unit testing, and CI/CD.
• Familiarity with object-oriented and functional programming practices.
• Experience developing RESTful APIs and microservices.
• Proven experience with cloud engineering on AWS, specifically SageMaker, Glue, Lake Formation, and Athena.
• Infrastructure as Code experience using CloudFormation, Terraform, or AWS CDK.
• Experience with graph technologies and knowledge graphs (e.g., Neo4j, Amazon Neptune).

Key Skills

Education

Any Gradute