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About This Role
DESCRIPTION
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This position is part of the AWS Specialist and Partner Organization (ASP). Specialists own the end\-to\-end go\-to\-market strategy for their respective technology domains, providing the business and technical expertise to help our customers succeed.
The Agentic WorkSpaces Solutions Architect team is seeking a hands\-on, customer\-obsessed Solutions Architect to accelerate customer adoption of agentic AI capabilities built on Amazon WorkSpaces. This team operates as a dedicated advisory and hands\-on development function — assigning engineering resources directly to customers to achieve production\-ready outcomes in weeks instead of months.
As an Applied AI Solutions Architect, you will be embedded with customers to help them prepare their Agentic WorkSpaces implementations for production. Your work centers on three pillars of agentic AI:
- Model Selection — Guiding customers through evaluating and selecting the right foundation models (via Amazon Bedrock) for their workspace use cases, balancing latency, accuracy, cost, and compliance requirements.
- Prompt Configuration — Designing, testing, and optimizing AI prompts and system instructions for agentic workspace AI, including self\-service agents, recommendation agents, and custom orchestrator agents.
- Tool Configuration — Architecting and building the tool integrations (APIs, Lambda functions, data connectors, knowledge bases) that agentic AI systems use to take actions on behalf of users — including configuring MCP (Model Context Protocol) servers for standardized tool discovery and invocation and enabling A2A (Agent\-to\-Agent) communication patterns for multi\-agent orchestration across enterprise systems.
- A critical dimension of this role is Customer Data Readiness — assessing, preparing, and structuring customer data assets so that AI agents can reliably access, retrieve, and act on the right information.
The delivery substrate for this work is Desktop Infrastructure (VDI) and Desktop\-as\-a\-Service (DaaS). You must understand the architecture, networking, identity, and operational patterns of virtualized desktop environments — because that is where these AI agents operate and where customers derive value.
You will work at the intersection of end\-user computing and applied AI, helping customers move from proof\-of\-concept to production for their Agentic WorkSpaces deployments.
This is a deeply technical, hands\-on role. You will write code, build integrations, configure agents, and pair\-program with customer engineering teams.
This role operates as forward deployed engineering. You will co\-build AI solutions directly alongside customer developers — not hand off reference architectures and walk away. Per the Customer Deployment Engineering ) methodology, you will navigate dev and test environments with the customer, validate configurations under real workload conditions, and identify production blockers before they surface. You will leverage these engagements to advise customers on production rollouts of WorkSpaces Agent Access — transitioning validated prototypes into fully operational agentic desktop environments at scale.
Willingness to travel up to 25–40% for on\-site customer engagements.
Key job responsibilities
You will lead technical discovery sessions with customer teams to understand business requirements, existing desktop infrastructure, and AI readiness. You translate findings into actionable implementation plans that move customers from evaluation to production deployment.
You will design and configure agentic AI solutions within Agentic WorkSpaces, including AI agent creation, prompt engineering, and tool/action integration.
You will build serverless integrations using AWS Lambda, API Gateway, Step Functions, and scripting (Python, Node.js).
You will architect secure access patterns to cloud\-based data systems (e.g. Amazon DynamoDB, Amazon RDS, Amazon S3, Knowledge Bases for Bedrock) to power AI agent tool use and retrieval\-augmented generation (RAG).
You will work with customers to deploy Amazon WorkSpaces Personal, Pools and Amazon WorkSpaces Applications as the delivery platform for agentic AI experiences.
You will guide customers through testing, evaluation, and validation of AI agent performance against defined success criteria before production deployment.
You will create reusable artifacts — reference architectures, implementation guides, sample code, prompt libraries, data readiness checklists — that scale best practices across the SA community and partner ecosystem.
You will provide Voice\-of\-Customer feedback to the Agentic WorkSpaces Service Team based on real\-world customer implementations, contributing to product roadmap prioritization.
A day in the life
You pair\-program with customer developers to build and test AI agent configurations deployed on WorkSpaces environments. You design prompt strategies and evaluate model performance across different foundation models. You configure MCP servers to expose customer APIs, databases, and tools in a standardized format for agent consumption. You design A2A workflows where workspace agents hand off to or collaborate with specialized agents across the customer’s enterprise. You deploy and validate WorkSpaces architectures to ensure the desktop substrate is production\-ready. You configure knowledge bases and data connectors for RAG\-powered agent responses. You conduct architecture reviews and provide prescriptive guidance for production readiness. You document implementation patterns and contribute to the team’s knowledge base. You participate in weekly syncs with service teams to share customer feedback and product insights.
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 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 with one of the following programming languages: Python, Ruby, Node.js, C\#, or C\+\+
- Hands\-on experience with agentic AI patterns — multi\-agent orchestration, tool use, function calling, chain\-of\-thought reasoning, and autonomous agent workflows.
- Familiarity with interoperability protocols such as MCP (Model Context Protocol) for standardized tool integration and/or A2A (Agent\-to\-Agent) for multi\-agent communication.
- Experience with Amazon Bedrock or equivalent foundation model platforms, including model invocation, agent creation, knowledge base configuration, and guardrails.
\- Understanding of Virtual Desktop Infrastructure (VDI) and Desktop\-as\-a\-Service (DaaS) technology — architecture, networking, identity, image management, and operational patterns.PREFERRED QUALIFICATIONS
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- 5\+ years of infrastructure architecture, database architecture and networking experience
- Experience implementing cloud services including migrations and modernization projects or similar
- Experience working with end user or developer communities
- 5\+ years of experience and practical knowledge with VDI and DaaS solutions, preferably Amazon WorkSpaces, AppStream 2\.0, Citrix, or VMware/Omnissa Horizon.
- Hands\-on experience building and deploying MCP servers — exposing enterprise tools and APIs via Model Context Protocol for dynamic agent tool discovery and invocation.
- Hands\-on experience with retrieval\-augmented generation (RAG), vector databases, and knowledge base configuration for production AI systems.
- AWS Solutions Architect certification (Associate or Professional) and/or AI Practitioner Certification.
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, FL, Miami \- 153,600\.00 \- 207,800\.00 USD annually
USA, GA, Atlanta \- 153,600\.00 \- 207,800\.00 USD annually
USA, IL, Chicago \- 153,600\.00 \- 207,800\.00 USD annually
USA, NY, New York \- 169,000\.00 \- 228,600\.00 USD annually
USA, WA, Seattle \- 153,600\.00 \- 207,800\.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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