Software Development Engineer, AI Upgrades & Transformation

Amazon Amazon · Big Tech · East Palo Alto, CA · Software Development

Software Development Engineer role focused on building and managing Agentic AI capabilities for AWS analytics services, specifically an Apache Spark Upgrade Agent that uses conversational interfaces and automated code transformation to reduce upgrade timelines. The role also involves managing a Model Context Protocol server for AI assistants and Agents, providing secure access to troubleshooting and code recommendation tools.

What you'd actually do

  1. Collaborate with experienced cross-disciplinary Amazonians to conceive, design, and bring innovative Agentic AI products and enable our customer to have seamless transformation experience.
  2. Design and build innovative technologies by applying Generative AI in a large distributed computing environment and help lead fundamental changes in the industry.
  3. Design and code the right solutions starting with broadly defined problems.
  4. Work in an agile environment to deliver high-quality software applying Generative AI.

Skills

Required

  • 3+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
  • 3+ years of non-internship professional software development 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
  • 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
  • Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques
  • Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution

What the JD emphasized

  • Agentic AI
  • Generative AI

Other signals

  • Agentic AI
  • Generative AI
  • automated code transformation
  • conversational interfaces