Software Development Engineer , Amazon Customer Service

Amazon Amazon · Big Tech · CA, BC +1 · Software Development

Software Development Engineer on the Data Intelligence team within Amazon Customer Service. The role focuses on designing and building robust, secure data infrastructure systems, including real-time data processing, secure storage, and privacy-compliant data access layers. A key responsibility is building infrastructure that supports the complete lifecycle of AI models, from development to production deployment, and collaborating with cross-functional teams to create enterprise-scale data processing systems and data products.

What you'd actually do

  1. Design and implement enterprise-scale data infrastructure and storage solutions that ensure optimal performance and reliability.
  2. Architect and build Machine Learning (ML) platform infrastructure that supports the complete model lifecycle, from training environments and validation frameworks to production deployment and monitoring systems.
  3. Develop and maintain robust data governance frameworks, implementing security controls, authentication mechanisms, and compliant data access patterns that protect sensitive information.
  4. Drive technical architecture decisions and system design, focusing on scalability, reliability, and performance of distributed services while ensuring alignment with business requirements.
  5. Own end-to-end delivery of technical solutions, including design, implementation, and verification of components, using standard software engineering methodologies and best practices.

Skills

Required

  • software development experience
  • system design and architecture
  • programming with at least one software programming language

Nice to have

  • full software development life cycle
  • coding standards
  • code reviews
  • source control management
  • build processes
  • testing
  • operations
  • computer science degree

What the JD emphasized

  • enterprise-scale data infrastructure
  • complete model lifecycle
  • production deployment
  • data governance frameworks
  • privacy-compliant data access layers

Other signals

  • ML platform infrastructure
  • model lifecycle
  • production deployment
  • data infrastructure systems
  • real-time data processing