As a member of Amazon's Worldwide Pricing and promotions team, you'll play a key role in the evolution of our Competitive Monitoring team to solve the world's most complex technical challenges in Crawling, Large-scale computing, Distributed systems, Web applications, and using Large language models in agentic workflows to build scalable, robust solutions at amazon scale. Key job responsibilities The Challenge Build the intelligence engine that powers Amazon's competitive advantage. We're looking for engineers who can architect real-time systems that ingest, analyze, and act on competitive data at scale - using machine learning, agentic workflows, and latest observability to solve problems that directly impact billions of shopping decisions. If you're energized by building systems that are simultaneously intelligent, resilient, and observable - and you want ownership of that entire stack - this is for you. What You'll Do The Competitive Monitoring team in Bangalore/Chennai is pioneering new approaches to competitive intelligence collection. You'll design systems that think by architecting agentic workflows that autonomously solve complex business problems while maintaining safety and observability end-to-end. You'll build for scale, engineering real-time, highly available solutions that handle Amazon's competitive landscape at massive volume. You'll own the full stack - from intuitive APIs and interfaces to robust backend systems, applying best practices in security, performance, accessibility, and usability. And you'll collaborate globally, partnering with teams in Seattle and across Amazon to shape the future of competitive intelligence. This is a growing, dynamic space where your work directly influences how customers discover products and make decisions. A day in the life A typical day involves: (a) Productionizing and maintaining ML models - deploying models to production and ensuring their ongoing reliability (b) Monitoring model health and data drift - developing algorithms to detect and address performance degradation and data quality issues (c) Accelerating the ML/AI path to production - partnering closely with scientists to streamline the journey from development to deployment (d) Building observability and alerting infrastructure - architecting tools to monitor, trace, and alert on AI model outputs