Sr. ML Engineer, Cait

Amazon Amazon · Big Tech · Seattle, WA · Solutions Architect

Senior ML Engineer focused on solving complex engineering problems to accelerate customer adoption of AI and Agentic platforms at scale. Responsibilities include architecting, designing, prototyping, and implementing reusable solutions for agent evaluation, multi-agent orchestration, semantic data layers, and cost optimization within business-critical situations.

What you'd actually do

  1. solve the most complex engineering problems that prevent our customers from successfully adoption AI at scale
  2. help customers build technically sound AI and Agentic platforms, addressing the most complex engienering problems in the process
  3. own ambiguous, high-impact engineering problems end-to-end — from architecture and design, to prototyping and skeletal production implementation
  4. combine innovative problem solving with the ability to package your innovations into reusable solution that the rest of the team can apply in customer-facing engagements

Skills

Required

  • 5+ years of non-internship professional software development experience
  • 5+ 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

  • 5+ 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

  • most complex engineering problems
  • complex engineering problems
  • business-critical situations
  • ambiguous, high-impact engineering problems

Other signals

  • customer adoption of AI
  • complex engineering problems
  • agent evaluation
  • multi-agent orchestration
  • semantic data layer
  • advanced cost optimization
  • AI stack
  • applying AI to difficult practical problems
  • business-critical situations
  • ambiguous, high-impact engineering problems end-to-end
  • architecture and design
  • prototyping
  • skeletal production implementation
  • reusable solution