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About This Role
DESCRIPTION
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AWS Global Sales drives adoption of the AWS cloud worldwide, enabling customers of all sizes to innovate and expand in the cloud. Our team empowers every customer to grow by providing tailored service, unmatched technology, and committed support. We dive deep to understand each customer's unique challenges, then craft innovative solutions that accelerate their success. This customer\-first approach is how we built the world's most adopted cloud. Join us and help us grow.
Are you passionate about Generative AI, Agentic AI, and Machine Learning? Are you passionate about helping customers design and build solutions leveraging the most comprehensive GenAI/ML platform available? Come join us!
At Amazon, we've been investing deeply in artificial intelligence for over 25 years, and many of the capabilities customers experience in our products are driven by machine learning. Amazon.com's recommendations engine is driven by ML, as are the paths that optimize robotic picking routes in our fulfillment centers. Our supply chain, forecasting, and capacity planning are informed by ML algorithms. Alexa is fueled by Natural Language Understanding and Automated Speech Recognition with deep learning. More recently, we've put generative AI at the core of every Amazon business, from coding assistants that help our developers ship faster, to AI agents that automate complex operational workflows, to foundation models that power entirely new customer experiences. We have thousands of engineers at Amazon committed to pushing the frontier of AI, and it's a big part of our heritage.
Within AWS, we bring that knowledge and capability to customers through three layers of the AI stack: 1\) AI Infrastructure with purpose\-built chips like AWS Trainium and Inferentia, GPU\-powered instances, and optimized frameworks like PyTorch and JAX, 2\) AI/ML Platforms including Amazon Bedrock for building generative AI applications with foundation models, agents, guardrails, and knowledge bases, and Amazon SageMaker AI for end\-to\-end model building, training, and deployment, and 3\) AI Application Services like Amazon Quick and Kiro (developer and business productivity), Amazon Nova foundation models, Amazon Transcribe, Amazon Textract, Amazon Comprehend, and Amazon Rekognition for quickly adding intelligence to applications.
AWS is looking for a GenAI/ML Solutions Architect who will be the Subject Matter Expert for helping customers in the United States design solutions leveraging our GenAI and ML services. You will work as an overlay to field sales teams, covering customers across multiple verticals and helping them architect and adopt solutions using Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker AI, Amazon Nova models, and the broader AI/ML portfolio. You will design RAG architectures, agentic AI workflows, model customization strategies, responsible AI implementations, and production\-scale inference pipelines. You will interact with other SAs in the field, providing guidance on their customer engagements. You will develop blog posts, reference implementations, workshops, and presentations to enable customers to fully leverage generative AI on AWS. Additionally, as the voice of the customer, you will work closely with service teams and submit product feature requests to drive the platform forward.
You must have deep technical experience working with technologies related to generative AI, machine learning, and/or deep learning. Hands\-on experience building applications on foundation models (RAG pipelines, agent frameworks, prompt engineering, model evaluation, fine\-tuning) is required. A strong mathematics and statistics background is preferred in addition to experience with solution architecture and production ML systems. You should be familiar with the GenAI ecosystem (model providers, orchestration frameworks, vector databases, evaluation tools) and will leverage this knowledge to help AWS customers evaluate tradeoffs and accelerate their AI adoption.
Travel up to 30% across the United States may be possible.
Key job responsibilities
Working with customers' development, data science, and AI engineering teams to deeply understand their business and technical needs. After understanding their needs, you will design solutions that make the best use of the AWS cloud platform and AWS AI/ML services including Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker AI, Amazon Nova foundation models, Amazon Quick, Kiro, Amazon Comprehend, Amazon Rekognition, Amazon Textract, and Amazon Transcribe.
Partner with SAs, Sales, Business Development, and the AI/ML service teams to accelerate customer adoption and revenue attainment in the AMERICAS for AWS generative AI and machine learning services, with a focus on Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker AI, and the agentic AI portfolio.
Thought Leadership: Evangelize AWS GenAI/ML services and share best practices through forums such as AWS blogs, whitepapers, reference architectures, sample code repositories, and public\-speaking events such as AWS Summit, AWS re:Invent, etc.
Act as a technical liaison between customers and the AWS Bedrock, AgentCore, SageMaker, and broader AI/ML service teams to provide customer\-driven product improvement feedback and feature requests.
Develop and support an AWS internal community of GenAI\-related subject matter experts in the AMERICAS, enabling field teams to identify, qualify, and position generative AI and agentic AI opportunities with their customers.
A day in the life
Most of your time is spent working directly with customers, helping them figure out how to use generative AI and machine learning to solve real business problems.
On a given morning, you might be on a video call with a team of engineers at a large insurance company who want to build an AI agent that can process claims documents. You're sketching out an architecture on a virtual whiteboard, asking questions about their data, and helping them think through tradeoffs between different approaches. That afternoon, you're prepping a demo for a different customer who's evaluating AWS against a competitor for a conversational AI use case. Later in the week, you're on\-site running a workshop where a customer's ML team is building their first retrieval\-augmented generation pipeline with you guiding them through it hands\-on.
You're typically focused on a single industry (think financial services, or healthcare, or manufacturing), so you build real familiarity with the problems, regulations, and data challenges in that space. You'll work with many different companies within your industry rather than being embedded at one or two for years. Some engagements last a few weeks, others stretch over a couple months, but the variety keeps things interesting.
Between customer conversations, you're building things that help others learn what you know: writing a blog post about a pattern you've seen work well, recording a short demo, or building a reference architecture that your peers across the country can reuse. You're also spending time helping other technical teams across the org understand how to spot AI/ML opportunities in their customer conversations.
Beyond the regular rhythm, some weeks bring unexpected moments that make this role special. You might get asked to present a customer success story to an audience of thousands at re:Invent, or get early access to a new service months before launch and help shape how it works based on what you've seen customers struggle with. Occasionally you'll find yourself in an executive briefing room explaining agentic AI to a Fortune 500 CTO who's deciding where to place a multi\-million dollar bet.
Travel runs about 20\-30%, mostly for customer workshops, executive briefings, and AWS events.
About the team
AWS Global Sales drives adoption of the AWS cloud worldwide, enabling customers of all sizes to innovate and expand in the cloud. Our team empowers every customer to grow by providing tailored service, unmatched technology, and support. We dive deep to understand each customer's unique challenges, then craft innovative solutions that accelerate their success. This customer\-first approach is how we built the world's most adopted cloud. Join us and help us grow.
About AWS
Diverse Experiences
AWS 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.
Inclusive Team Culture
AWS values curiosity and connection. Our employee\-led and company\-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do.
Mentorship \& 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.
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 we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.
BASIC QUALIFICATIONS
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- 8\+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data \& analytics) experience
- 3\+ years of design, implementation, or consulting in applications and infrastructures experience
- Experience in a technical role within a sales organization
PREFERRED QUALIFICATIONS
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- 5\+ years of IT development or implementation/consulting in the software or Internet industries experience
- Experience working with end user or developer communities
- Experience communicating across technical and non\-technical audiences, including executive level stakeholders or clients
- Experience architecting/operating solutions built on AWS
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
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, CA, San Francisco \- 176,600\.00 \- 239,000\.00 USD annually
USA, IL, Chicago \- 153,600\.00 \- 207,800\.00 USD annually
USA, MA, Boston \- 153,600\.00 \- 207,800\.00 USD annually
USA, NY, New York \- 169,000\.00 \- 228,600\.00 USD annually
Salary Context
This $153K-$207K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Amazon Web Services, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($180K) sits 16% below the category median. Disclosed range: $153K to $207K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Amazon Web Services AI Hiring
Amazon Web Services has 93 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer, AI Software Engineer, AI Product Manager. Positions span New York, NY, US, Arlington, VA, US, Cupertino, CA, US. Compensation range: $160K - $350K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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