Software Development Engineer, Amazon Quick Suite

Amazon Amazon · Big Tech · Seattle, WA · Software Development

Software Development Engineer role focused on building and optimizing retrieval-augmented generation (RAG) systems for intelligent document processing and search within Amazon Quick Suite. The role involves designing scalable ingestion pipelines for multi-modal content, optimizing system performance, and collaborating on feature delivery, leveraging AWS technologies.

What you'd actually do

  1. designing, developing, testing, and deploying distributed machine learning systems and large-scale solutions
  2. collaborate closely with your team to influence our overall strategy and define the team’s road map
  3. drive the system architecture, spearhead best practices that enable a quality product
  4. help coach and develop junior engineers

Skills

Required

  • 3+ years of non-internship professional software development experience
  • 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
  • 1+ years of software development engineer or related occupational experience
  • 1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience
  • 1+ years of Object Oriented Design experience
  • Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
  • Experience programming with at least one software programming language

Nice to have

  • 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
  • Bachelor's degree in computer science or equivalent

What the JD emphasized

  • large-scale software systems
  • distributed machine learning systems
  • large-scale solutions
  • multi-modal content
  • distributed systems challenges at scale
  • large-scale software systems
  • complex distributed systems issues
  • architectural decisions
  • technical direction of a product
  • scalable ingestion pipelines
  • multiple modalities
  • advanced computer vision and machine learning techniques
  • technical excellence
  • operational rigor

Other signals

  • retrieval-augmented generation
  • multi-modal content processing
  • large-scale distributed systems
  • machine learning systems