Senior Software Engineer, Amazon's Talent Solution’s Core Science and Engineering

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

Senior Software Engineer role focused on building and scaling MLOps/GenAIOps systems for Amazon's Talent Solutions. The role involves managing large data volumes, supporting the ML lifecycle (training, validation), and enhancing LLM responses using techniques like RAG and Prompt Engineering. Emphasis on scalable systems for model deployment and GenAI application building.

What you'd actually do

  1. As a Software Engineer, you'll team up with economists, academic scholars, machine learning experts, and fellow developers throughout the company.
  2. Together, you'll craft, experiment, and launch services embodying diverse scientific models.
  3. This involves leveraging intricate distributed systems, handling and presenting Big Data, and applying advanced statistical methods.
  4. The ideal candidate will thrive on innovation, embrace leading-edge tech, and relish contributing to a high-impact field.

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

  • familiarity with Machine Learning lifecycle management
  • model training
  • validation
  • debugging tools
  • analysis techniques
  • enhancing LLM responses through methodologies like RAG, Prompt Engineering
  • robust software engineering fundamentals
  • system architecture
  • strong grasp of machine learning principles
  • capability to construct scalable systems that streamline model deployment and GenAI application building
  • Exposure to ML system design
  • frameworks
  • GenAI application design/development
  • industry best practices
  • 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

  • managing large volumes of data within an MLOps/GenAIOps framework
  • Machine Learning lifecycle management
  • enhancing LLM responses through methodologies like RAG, Prompt Engineering
  • construct scalable systems that streamline model deployment and GenAI application building

Other signals

  • MLOps/GenAIOps framework
  • Machine Learning lifecycle management
  • LLM responses through methodologies like RAG, Prompt Engineering
  • scalable systems that streamline model deployment and GenAI application building