Software Development Engineer, Amazon Q

AI in this role

ai-agents

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 AWS context, retrieval, tools, and actions to help customers understand their environments and get work done faster.

Building these experiences creates challenging engineering problems. How do you ground an LLM's response in relevant and up-to-date information from a customer's AWS environment while maintaining strong security boundaries and low latency? How do you build agentic experiences that can reason, retrieve information, invoke tools, and take actions reliably and safely? How do you measure and improve the quality of AI-powered experiences when traditional software testing alone is not enough?

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

In this role, you will own meaningful features and components from design through production. You will work closely with other engineers, product managers, applied scientists, UX partners, and teams across AWS to translate customer needs into scalable technical solutions. You will contribute to architectural decisions, implement production-quality software, and continuously improve the reliability, performance, and quality of the systems you own.

This is an opportunity to work on emerging generative AI technologies while solving real distributed-systems problems at AWS scale.

Key job responsibilities
As a Software Development Engineer II on the Amazon Q team, you will:

Build: Design, implement, test, and launch customer-facing Amazon Q capabilities and the backend services that power them. Build systems that connect foundation models with AWS knowledge, customer context, retrieval systems, APIs, tools, and actions.

Solve: Work independently on complex engineering problems involving generative AI, distributed systems, contextual grounding, retrieval, tool execution, agentic workflows, latency, reliability, and security.

Own: Take ownership of features and services throughout their lifecycle—from requirements and technical design through implementation, deployment, operations, troubleshooting, and continuous improvement.

Design: Contribute to system architecture and technical designs, evaluate engineering trade-offs, participate in design reviews, and help build solutions that are scalable, maintainable, secure, and cost effective.

Operate: Build reliable production systems with strong engineering practices around testing, observability, monitoring, deployment, operational readiness, and incident response. Use operational and customer signals to identify and address opportunities for improvement.

Collaborate and innovate: Work closely with engineers, applied scientists, product managers, and partner teams to bring advances in foundation models, retrieval, reasoning, and agentic AI into production. Participate in code reviews, share technical knowledge, and help raise the engineering quality of the team.

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.

Basic qualifications

- 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
- 1+ years of software development engineer or related occupational experience
- 1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience
- 1+ years of Object Oriented Design experience
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
- Experience programming with at least one software programming language

Preferred qualifications

- 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

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, VA, Arlington - 143,700.00 - 194,400.00 USD annually

How we rate this

Software Development Engineer, Amazon Q at Amazon rates 83 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

AI Agents

Questions you could be asked

How do you decide when an AI agent can act on its own versus asking for approval first?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: AI Agents. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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