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About This Role
DESCRIPTION
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Are you passionate about the future of intelligent desktops and autonomous workplace technology? Do you want to lead a team that is defining how enterprises deploy, manage, and transform end\-user computing through agentic AI? Amazon Web Services (AWS) is looking for a Senior Manager of Solutions Architecture to lead our Worldwide Agentic Workspaces Solutions Architecture team within Applied AI Solutions (AAIS).
In this role, you will lead a globally distributed team of Specialist Solutions Architects who serve as the primary technical advisors to enterprise customers adopting Amazon WorkSpaces, WorkSpaces Thin Client, and our emerging portfolio of agentic desktop and automation solutions. You will own the technical strategy, customer engagement model, and field enablement for the Agentic Workspaces SA practice, partnering closely with the Service Team, GTM/Business Development, and Product leadership to drive adoption at scale.
The Agentic Workspaces business is substantial and growing rapidly. You will define how Solutions Architects engage customers across desktop\-as\-a\-service, intelligent workspace automation, and AI\-powered end\-user computing. Your team will build the technical bar for customer engagements, develop reference architectures, and pioneer new solutions at the intersection of end\-user computing and agentic AI.
This is a role for a technical leader who can operate at the intersection of deep infrastructure expertise and emerging AI capabilities, someone who has led end\-user computing or desktop virtualization programs at scale and is now ready to shape the next generation of intelligent, autonomous workspace solutions.
Key job responsibilities
- Lead and develop a high\-performing team\*\* of Specialist Solutions Architects focused on Agentic Workspaces and end\-user computing, providing technical mentorship, career development, and performance management across multiple geographies.
- Own the worldwide SA strategy\*\* for Agentic Workspaces, defining engagement models, technical readiness programs, and customer success frameworks that scale globally across AMER, EMEA, and APJ.
- Serve as the senior technical advisor\*\* to enterprise customers evaluating and deploying Amazon WorkSpaces, WorkSpaces Thin Client, and agentic desktop solutions, engaging directly with CIOs, CTOs, and VP\-level technical decision\-makers.
- Drive customer adoption and competitive defense\*\* by developing differentiated architectures, proof\-of\-concept engagements, and migration strategies that position AWS favorably against Citrix, VMware Horizon, Microsoft AVD/W365, and emerging AI\-native workspace providers.
- Pioneer agentic workspace solutions\*\* by partnering with the Service Team and Product to define how autonomous AI agents integrate with desktop environments, enabling intelligent automation, self\-healing workspaces, and AI\-assisted end\-user experiences.
- Build and scale field enablement programs\*\* including training, certification, solution assets, and reusable architectures that multiply SA expertise across the broader AWS field organization and partner ecosystem.
- Influence product roadmap\*\* by synthesizing customer feedback, competitive intelligence, and field insights into actionable recommendations for Service Team leadership, ensuring the product evolves to meet enterprise requirements.
- Establish and maintain the technical bar\*\* for the team through architecture reviews, design standards, and engagement quality frameworks that ensure consistent, world\-class customer outcomes.
- Partner cross\-functionally\*\* with GTM/Business Development, Partner SA, Professional Services, and Account teams to drive pipeline creation, accelerate deal velocity, and expand the Agentic Workspaces footprint across strategic accounts.
- Represent AWS externally\*\* as a thought leader through customer executive briefings, re:Invent sessions, blog posts, and industry engagements that establish AWS as the leader in intelligent end\-user computing.
A day in the life
Your morning might start with a strategic architecture review for a Fortune 500 financial services customer migrating 50,000 desktops from a legacy solution to Amazon WorkSpaces with AI\-powered automation. Mid\-morning, you coach one of your Solutions Architects through a complex proof\-of\-concept involving agentic desktop assistants that autonomously resolve IT tickets and optimize workspace performance. Over lunch, you review the team's competitive win/loss analysis and refine your displacement playbook.
In the afternoon, you join a product roadmap review with the WorkSpaces Service Team, advocating for features your largest customers need. You then prepare your team's contribution to the upcoming re:Invent session on the future of intelligent desktops. Before wrapping up, you hold a 1:1 with a team member in EMEA, discussing their path to Principal SA and reviewing their approach to a multi\-region deployment architecture for a global manufacturing customer.
About the team
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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- 7\+ years of management of technical, enterprise customer facing resources or equivalent experience
- 7\+ years of infrastructure architecture, database architecture and networking experience
- Bachelor's degree
\- Deep technical knowledge of end\-user computing technologies including desktop virtualization (VDI), desktop\-as\-a\-service (DaaS), workspace management, and endpoint securityPREFERRED QUALIFICATIONS
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- Experience architecting, migrating, transforming or modernizing customer requirements to the cloud
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 \- 221,100\.00 \- 299,200\.00 USD annually
USA, VA, Arlington \- 201,000\.00 \- 272,000\.00 USD annually
USA, WA, Seattle \- 201,000\.00 \- 272,000\.00 USD annually
Salary Context
This $201K-$299K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($250K) sits 35% above the category median. Disclosed range: $201K to $299K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Amazon Web Services AI Hiring
Amazon Web Services has 80 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, AI Agent Developer, Data Scientist. Positions span New York, NY, US, Seattle, WA, US, Bellevue, WA, US. Compensation range: $177K - $299K.
Location Context
AI roles in New York pay a median of $211,000 across 2,760 tracked positions. That's 5% above the national median.
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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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