Software Development Engineer - Trust and Privacy, Devices & Services Trust, Privacy, and Accessibility (tpa)

Amazon Amazon · Big Tech · IN, KA, Bengaluru · Software Development

Software Development Engineer to architect and scale Gen AI-powered platforms and development tools that champion trustworthy customer experiences, privacy-by-design principles, and organizational trust at scale. This role will help set the standard for how privacy and trust technologies are implemented across D&S, driving end-to-end adoption of solutions that make trustworthy customer experiences the automatic choice for teams across Amazon.

What you'd actually do

  1. Architect and develop next-generation AI solutions including agentic AI systems, multi-agent orchestration frameworks, and Model Context Protocol (MCP) implementations
  2. Lead initiatives in fine-tuning large language models, designing retrieval-augmented generation (RAG) architectures, and establishing enterprise-scale vector database infrastructures optimized for semantic search and AI workloads
  3. Architect and deploy production-grade services on AWS using cloud-native architectures, implementing infrastructure-as-code and engineering best practices to support highly scalable and reliable systems
  4. Design and implement centralized tooling platforms and automation systems that enable engineering teams to move fast while maintaining high quality standards
  5. Mentor engineers and influence engineering culture around software development best practices, system design, and technical innovation

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
  • Experience programming with at least one software programming language
  • Experience building complex software systems that have been successfully delivered to customers

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
  • 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 systems
  • multi-agent orchestration
  • fine-tuning large language models
  • retrieval-augmented generation (RAG) architectures
  • enterprise-scale vector database infrastructures

Other signals

  • Gen AI-powered platforms
  • agentic AI systems
  • fine-tuning large language models
  • retrieval-augmented generation (RAG) architectures
  • enterprise-scale vector database infrastructures