Sr Software Development Engineer, Amazon Q

Amazon Q is helping redefine how developers and cloud practitioners build, operate, troubleshoot, and optimize on AWS. We are building an intelligent assistant that goes beyond answering questions-combining generative AI with deep AWS context, retrieval, tools, and actions to help customers understand their environments and get work done faster.

Building this experience introduces a new class of engineering challenges. How do you ground an LLM"s response in relevant, up-to-date information from a customer"s AWS environment while maintaining strong security boundaries and low latency? How do you orchestrate multi-step agentic workflows that can reason, retrieve information, invoke tools, and take actions reliably and safely? How do you evaluate AI systems where quality cannot be measured by traditional software testing alone? These are the kinds of problems you will help solve.

We are looking for a Senior Software Development Engineer to help shape the next generation of Amazon Q. You will work at the intersection of generative AI, agentic systems, and large-scale distributed services to build highly visible, customer-facing capabilities used across AWS.

As a Senior SDE, you will provide technical leadership for complex and ambiguous initiatives. You will work closely with engineers, product managers, applied scientists, UX partners, and teams across AWS to define architecture, make critical technical decisions, and translate advances in foundation models, retrieval, reasoning, and agents into secure, reliable production experiences.

This role offers an opportunity to influence both what we build and how we build it-developing new architectures and engineering mechanisms for AI-powered systems while raising the technical bar across the team.

Key job responsibilities

As a Senior Software Development Engineer on the Amazon Q team, you will:

Build: Design and deliver major Amazon Q capabilities and the highly available, scalable, secure, and low-latency distributed systems that power them. Build systems that connect foundation models with customer context, AWS knowledge, retrieval systems, APIs, tools, and actions.

Solve: Tackle ambiguous engineering problems unique to production generative AI, including contextual grounding, agent orchestration, tool execution, retrieval, security boundaries, latency, reliability, and mechanisms for evaluating AI quality and effectiveness.

Own: Lead projects end to end-from requirements and architecture through implementation, launch, operations, measurement, and continuous improvement-making thoughtful trade-offs across customer experience, quality, scalability, availability, security, performance, and cost.

Lead: Define technical direction for complex initiatives, influence architecture across Amazon Q and partner teams, and simplify systems as they evolve. Drive high engineering standards across design, testing, observability, deployment, and operational excellence.

Mentor: Raise the technical bar of the organization through design reviews, code reviews, technical discussions, and hands-on development. Mentor engineers and help teams navigate complex architectural and implementation decisions.

Innovate: Work with applied scientists, product leaders, and engineering teams to bring advances in foundation models, retrieval, reasoning, and agentic AI into production. Explore new architectures and mechanisms that improve the quality, safety, reliability, latency, and usefulness of AI-powered customer experiences.

A day in the life

You"ll start the day with the signals from how Amazon Q performed yesterday - satisfaction trends, latency tails, eval results - and pick up the problem that matters most. Mornings might be a design review with applied scientists on retrieval or agent orchestration; afternoons, writing the service code that ships it. You"ll partner with PMs, TPMs, and AWS service teams onboarding their own capabilities into the assistant, and you"ll own what you build in production. Expect to move between deep distributed-systems work, prompt and evaluation iteration, and A/B experiments that tell you whether your change actually helped a customer.

About the team

We"re the team behind Amazon Q in the AWS Management Console - engineers, applied scientists, and product managers working in one loop rather than in handoffs. Our mission is simple to say and hard to do: make the assistant genuinely useful to anyone operating on AWS. We ship fast, measure honestly, and say so when the data doesn"t support the idea we liked. Because the field moves monthly, we expect to rewrite our own assumptions often, and we"d rather learn from a real experiment than argue in a doc. We care about operational excellence, and about each other"s time.