Software Development Engineer , Amazon Customer Service

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

Software Development Engineer role focused on building and maintaining data infrastructure and ML platform infrastructure to support the complete AI model lifecycle, from development to production deployment, within Amazon Customer Service.

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
  • architecture
  • data infrastructure
  • ML platform infrastructure
  • model lifecycle support
  • production deployment
  • monitoring systems
  • data governance
  • security controls
  • authentication mechanisms
  • data access patterns
  • scalability
  • reliability
  • performance
  • distributed services
  • software engineering methodologies
  • best practices
  • coding standards
  • code reviews
  • source control management
  • build processes
  • testing
  • operations

Nice to have

  • Bachelor's degree in computer science

What the JD emphasized

  • enterprise-scale data processing systems
  • scalable data products
  • high-scale environments
  • robust data and ML infrastructure solutions
  • critical AI initiatives
  • new AI models
  • data and ML platforms
  • data compliance requirements
  • model deployment workflows

Other signals

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