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About This Role
DESCRIPTION
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As part of the AWS Applied AI Solutions organization, we have a vision to provide business applications, leveraging Amazon’s unique experience and expertise, that are used by millions of companies worldwide to manage day\-to\-day operations. We will accomplish this by accelerating our customers’ businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon’s real\-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no\-brainers to buy and easy to use.
Lead our pioneering AI initiative at AWS and define the future of AI agents. Shape how businesses leverage artificial intelligence by creating foundational building blocks used across AWS business applications.
The Core Services team within AWS Applied AI Solutions is creating the foundations that will power the next generation of AI agents from small business to enterprise scale. As a technical leader, you'll lead our new agentic AI building blocks initiative, pioneering the development of reusable AI components that accelerate and standardize AI product delivery across AWS business applications such as contact center, supply chain, healthcare and life sciences. You'll own one critical capability area such as agent identity and governance, agent collaboration \& orchestration, agent evaluation, or knowledge management, and have the privilege to define what we build and how we build it.
This role combines the excitement of a startup environment with the scale of AWS. You'll research state\-of\-the\-art open source and internal tools, tackle highly ambiguous problems, and lead a team building a V1 product from the ground up. You won't just be implementing someone else's vision — you'll chart the course, define the roadmap, and create solutions that eliminate non\-differentiating work while ensuring enterprise\-grade quality and consistency. If you thrive on ownership, are passionate about AI, and want to fundamentally influence how AWS builds AI products, this role offers an extraordinary opportunity to make your mark.
Key job responsibilities
- Own the technical architecture and strategy for a critical AI agent capability area, setting the foundation for enterprise\-scale AI solutions across AWS
- Lead projects requiring multiple engineers, balancing business goals with technical excellence while navigating ambiguous problem spaces
- Research, evaluate, and integrate state\-of\-the\-art AI technologies, making informed decisions about build vs. leverage approaches
- Design and implement reusable AI components that meet enterprise\-grade quality standards while simplifying complex technical challenges
- Drive consensus across teams with different priorities and perspectives to create unified AI building blocks
- Champion engineering best practices, establishing a culture of robust software development with exemplary code organization, clarity, and maintainability
- Mentor and coach other engineers, fostering technical growth while creating an environment where your team thrives independently
- Communicate complex technical designs to diverse audiences, from engineers to non\-technical stakeholders and senior leadership
A day in the life
Your morning starts with an engineering team standup, followed by collaborative sessions with product managers in your team to provide requirement and internal customer inputs. You'll dedicate focused time to researching the latest AI agent techniques and designing reusable, scalable building blocks for your capability area. Throughout the day, you'll work with engineers and scientists within your team to implement solutions, leveraging coding agents to write and test code more efficiently. You might lead a technical design review with scientists, engineers, product managers, solution architects from across Applied AI Solutions, gathering diverse perspectives to strengthen your approach. Your day could include architecture, prompt, component, API design work, meeting with internal customers to understand their AI agent needs, or presenting your vision to leadership stakeholders.
About the team
ABOUT AWS:
Diverse Experiences
Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Work/Life Balance
We value work\-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee\-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity and AmazeCon conferences, inspire us to never stop embracing our uniqueness.
Mentorship and Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge\-sharing, mentorship and other career\-advancing resources here to help you develop into a better\-rounded professional.
BASIC QUALIFICATIONS
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- 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
- Experience with full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations
- Experience designing and implementing AI/ML systems, including working with LLMs, prompt engineering, retrieval augmented generation (RAG), fine\-tuning, or AI agent development
- Proven track record building distributed systems with well\-designed APIs that operate reliably at enterprise scale
- Demonstrated ability to create reusable software components with clean interfaces that can be leveraged by multiple teams
- Strong working knowledge of AWS AI/ML services such as Amazon Bedrock, SageMaker, and related infrastructure
- Successful history of cross\-organizational collaboration, driving technical convergence while maintaining delivery velocity
PREFERRED QUALIFICATIONS
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- 10\+ 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
- Experience designing and implementing evaluation systems that can measure AI system performance, and experience with metrics and testing methodologies.
- Experience building hierarchical memory/storage systems for AI products, including schema design for storing and retrieving context, and memory reuse
- Experience developing tools and frameworks that manage prompts.
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, WA, Seattle \- 168,100\.00 \- 227,400\.00 USD annually
Salary Context
This $168K-$227K range is above the median for AI Software Engineer roles in our dataset (median: $190K across 193 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,824 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Amazon.com, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $234,620 based on 682 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($197K) sits 16% below the category median. Disclosed range: $168K to $227K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Amazon.com AI Hiring
Amazon.com has 98 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, AI Product Manager, Data Scientist. Positions span New York, NY, US, Seattle, WA, US, Sunnyvale, CA, US. Compensation range: $101K - $300K.
Location Context
AI roles in Seattle pay a median of $228,000 across 1,009 tracked positions. That's 14% above the national median.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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