Sr Sde, Amazon India Advertising

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

Senior Software Engineer role focused on building and deploying ML/AI models for digital advertising, including recommendation, ads ranking, personalization, and search. The role involves the full ML lifecycle from data preparation and fine-tuning to production deployment and monitoring, with an emphasis on scalable, low-latency systems and leveraging generative AI and LLMs.

What you'd actually do

  1. You will get an opportunity to work on building large-scale machine-learning infrastructure for online recommendation, ads ranking, personalization, and search — including designing, training, evaluating, and deploying ML/AI models end-to-end.
  2. You will leverage AI-assisted development tools and practices (e.g., code generation, automated testing, AI-powered code review) to accelerate engineering velocity and code quality.
  3. You will drive appropriate technology choices for the business — including evaluating and integrating generative AI, LLMs, and foundation models — leading the way for continuous innovation and shaping the future of India Advertising.
  4. You will be fluent in the AI coding lifecycle: from data preparation, prompt engineering, and model fine-tuning through to responsible AI practices, model monitoring, and iterative improvement in production.
  5. You will own operating our products, driving excellence in feature stability, performance, flexibility, and responsible AI governance.

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
  • Bachelor's degree in computer science or equivalent

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

What the JD emphasized

  • highly innovative, scalable, high-performance and low latency solutions
  • building large-scale machine-learning infrastructure
  • end-to-end
  • generative AI, LLMs, and foundation models
  • AI coding lifecycle
  • responsible AI practices
  • high volume and low latency distributed systems

Other signals

  • building large-scale machine-learning infrastructure
  • designing, training, evaluating, and deploying ML/AI models end-to-end
  • integrating generative AI, LLMs, and foundation models
  • AI coding lifecycle: data preparation, prompt engineering, model fine-tuning, responsible AI practices, model monitoring, and iterative improvement in production
  • implementing key functional areas of the customer experience, including website applications, platform services, and AI-powered features