Sr. SDE, Agentic Systems, Technical Content Experience (TCX) Engineering

Amazon is hiring a Sr. SDE, Agentic Systems, Technical Content Experience (TCX) Engineering in New York, United States. It pays $185k-$250k a year and Level rates it ; you can apply on Amazon.

Level's read on this job

On Level's scale, this software engineer role is Builds AI ●●●●: the job is building AI systems.
The listed pay starts 10% above the typical software engineer posting on Level.
Tools and skills the posting cites include AI agents.
Level classifies it as a senior role.
Level lists 21,000+ other open roles at Amazon.

AI in this role

ai-agents

AWS Documentation, one of the most visited technical documentation sites in the world, is increasingly authored using AI agents; not as an experiment, but in production. You'll be the senior engineer owning the architecture behind it: a runtime that executes documentation workflows autonomously, multi-agent orchestrated authoring tools that help service teams produce documentation from their design documents and service models, and analyzers that review those contents against AWS standards before commit.

This is hard in interesting ways: agent that writes plausible-sounding API documentation is worse than no agent at all, so correctness and verifiability sit at the heart of the architecture. You'll decide how specialized agents divide work versus deterministic orchestration with agentic reasoning at the leaves, how their output gets evaluated, and when a workflow needs to escalate to a human. In a domain where agent patterns change every few months, you know that today's right call has a short shelf life and adapt your own orchestration patterns, tool ecosystem design, and inference cost and latency.

Shape technical strategy where ambiguity spans both product shape and technical approach, lead initiatives through engineers and teams you don't control, and ship weekly while doing it.

Key job responsibilities
- Own the architecture across the portfolio — hosted agent runtime, multi-agent authoring workflows, commit-time content analyzers, and operational telemetry — making technology choices where neither the product shape nor the technical approach is defined
- Design the correctness and verifiability layer: ground agent output in authoritative sources, catch fabrication, and define when a workflow escalates to human review
- Lead projects requiring work from multiple engineers, including agent orchestration patterns, evaluation frameworks, bidirectional XML transformation, and developer-facing authoring tools
- Turn content quality evaluations into feedback signals that measurably improve agent behavior, and influence architectural decisions in adjacent systems your services depend on but don't control
- Raise the engineering bar through design reviews, exemplary code, and mentoring engineers newer to production AI systems, while identifying root causes of systemic issues and balancing delivery speed against long-term system quality

A day in the life
You'll start with yesterday's agent runs - did the research agent find the right sources, did the writing agent produce content that passes style review? You're asking whether the orchestration architecture still holds as model capabilities shift.

Mid-morning, a service team needs documentation generated from a model type your pipeline doesn't handle. Neither the product shape nor the approach is obvious, so you design the pattern and prototype it.

After lunch, a partner team's API redesign affects how your agents integrate. You influence that design to serve both human and agentic workflows - building consensus without owning it.

About the team
The Applied Science team builds the AI-powered tools that are fundamentally changing how AWS documentation gets created. We develop and operate multi-agent content generation systems, intelligent code-review analyzers, developer-facing authoring tools, and the telemetry infrastructure that measures it all.

Our tools are used daily by hundreds of contributors across AWS. We move fast — shipping improvements weekly — while maintaining the reliability that production content systems require. The team values pragmatic engineering, measurable impact, and the intellectual honesty to know when an AI approach isn't working and to try something else.

We're part of TCX Engineering, the organization behind the AWS Documentation website and the AI-first content platform powering it. You'll work closely with product managers, content strategists, and peer engineering teams building the broader platform infrastructure.

Basic qualifications

- 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

Preferred qualifications

- Master's degree in computer science or equivalent
- 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution

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, NY, New York - 184,900.00 - 250,200.00 USD annually

How we rate this

Sr. SDE, Agentic Systems, Technical Content Experience (TCX) Engineering at Amazon rates 87 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. Read the description and count the AI tools, models and workflows it names.

Key facts

Sr. SDE, Agentic Systems, Technical Content Experience (TCX) Engineering is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $185k-$250k a year.
The role is based in US, NY, New York.
The job was posted on September 9, 2026.
Seniority: senior.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI agents

Also common in software engineer jobs at this level

Python C++Kubernetes Java Generative AI TypeScript

Pay

This job starts at: $185k
Typical software engineer job: $168k

This posting starts 10% above the typical software engineer job. See software engineer jobs

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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