Join Fulfillment by Amazon (FBA) and contribute to building AI-native engineering systems that redefine operational efficiency. FBA empowers sellers globally with Amazon's robust logistics infrastructure, serving businesses of all sizes across numerous countries.

This role focuses on creating innovative AI systems to eliminate reactive tasks. Instead of managing tickets or troubleshooting deployments, you will engineer solutions that make operational issues disappear. Imagine agents that resolve problems before they reach humans, self-healing pipelines that deploy autonomously, and a knowledge base enabling software to answer its own questions.

You will be part of a VP-funded, multi-year initiative with comprehensive ownership. This is an opportunity to work at the forefront of applied AI, developing LLM-powered agents, machine-readable operational knowledge, and autonomous deployment systems. Your work will directly impact over 55 teams and 500 engineers within Amazon.

Develop AI-native tooling designed to eliminate reactive operational tasks for other engineering teams.

Implement LLM-powered remediation agents capable of detecting, diagnosing, and resolving operational issues automatically.

Build self-healing deployment platforms that enable fully continuous, hands-off delivery through safe primitives and automated integration testing.

Construct the FBA knowledge foundation, utilizing OE infrastructure and knowledge-graph tooling to enable AI systems to reason about FBA services and autonomously answer operational questions.

Contribute to system architecture and design, focusing on reliability and scalability.

Possess at least three years of professional software development experience, excluding internships.

Demonstrate proficiency in at least one modern programming language such as Java, C++, or Python.

Have experience contributing to the architecture and design of new and existing systems, including design patterns, reliability, and scaling.

Exhibit experience with the complete software development lifecycle, covering coding standards, code reviews, source control, build processes, testing, and operations.

Familiarity with building automation, platform, or developer-productivity systems like CI/CD or observability is a plus.

A strong interest in applying LLMs and AI-native approaches to practical engineering challenges is preferred.

Understanding of continuous delivery and autonomous deployment practices is beneficial.