Sr. Software Dev Engineer, Applied AI

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

Senior Software Development Engineer on the Applied AI team, focusing on Knowledge Work Automation. The role involves designing, building, and shipping multi-agentic systems and AI agents for internal Amazon workflows, leveraging LLMs and production-grade distributed software. Key responsibilities include architecting agentic AI systems, innovating with LLM techniques, shipping reusable primitives, and driving end-to-end delivery.

What you'd actually do

  1. Architect & build agentic AI systems
  2. Innovate at the frontier of AI
  3. Ship reusable primitives
  4. Own end-to-end delivery
  5. Work cross-functionally

Skills

Required

  • 6+ years of non-internship professional software development experience
  • 6+ years of programming with at least one software programming language experience
  • 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
  • Experience as a mentor, tech lead or leading an engineering team

Nice to have

  • 7+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
  • Experience building LLM-powered applications, multi-agent systems, or AI/ML production pipelines
  • Experience with AI/ML frameworks and tools (e.g., LangChain, LangGraph, Amazon Bedrock, SageMaker, vector databases, RAG architectures)
  • Experience designing APIs and platform primitives consumed by other engineering teams
  • Track record of mentoring engineers and raising the technical bar on a team
  • Strong ability to operate in ambiguous, fast-moving problem spaces and make pragmatic trade-offs
  • Experience with event-driven architectures, workflow orchestration (Step Functions, Temporal, or equivalent), and container-based development
  • Bachelor's degree in Computer Science, Engineering, or equivalent experience
  • Publications, patents, or demonstrable contributions in AI/ML or distributed systems

What the JD emphasized

  • multi-agent systems
  • agent orchestration
  • tool-augmented reasoning
  • production-grade software
  • Amazon scale

Other signals

  • building multi-agent systems
  • shipping AI agents
  • production-grade software
  • Amazon scale