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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. Partner teams own the strategy, recruiting, development, and growth of our key technology and consulting partners. Together they provide our customers with the expertise and scale needed to build innovative solutions for their most complex challenges.
Our vision in the Applied AI Solutions Go\-To\-Market team is to be a leading provider of AI and Agentic business applications, leveraging Amazon's unique experience and expertise, used by millions of companies around the world to manage their day\-to\-day operations. Our mission is to accelerate our customers' businesses by delivering intuitive and differentiated technology solutions that solve enduring business challenges.
We are seeking a technical program manager with deep AI expertise to join the Applied AI Solutions GTM team. This role will drive organizational effectiveness through hands\-on development of AI\-powered tools, automation, and knowledge systems that transform how our GTM organization operates. You will be the technical force multiplier for our team, building innovative solutions using Amazon QuickSight, Kiro, and other AI technologies to streamline processes, enhance account planning, and accelerate business growth.
This role requires expertise in generative AI technologies, hands\-on experience building with Amazon QuickSight (including knowledge spaces, flows, and Quick Pages), and the ability to translate business needs into technical solutions. You will own the technical roadmap for AI innovation within our GTM organization, compile monthly business reviews, and serve as technical program manager for cross\-functional initiatives across GTM Acceleration and Applied AI Solutions.
You must be passionate about leveraging AI as a daily force multiplier—not just for writing and chatting, but for automating repeatable work, performing analysis, and accelerating action. You should be energized by prototyping new capabilities, turning one\-off ideas into durable team assets, and continuously raising the bar for how AI can enhance GTM operations.
The ideal candidate is an exceptional writer with Amazonian document\-writing skills, a natural organizer who thrives on bringing structure to ambiguity, and someone who sees AI as a daily force multiplier for operational excellence.
Key job responsibilities
- Build and maintain AI\-powered tools and automation using Amazon QuickSight, Kiro, and other generative AI technologies to streamline GTM processes, account planning, and organizational workflows.
- Design and implement shared knowledge spaces in Amazon QuickSight, ensuring playbooks, account context, and customer insights are discoverable and reusable across field and partner teams.
- Prototype innovative solutions using Quick Pages, Kiro, and emerging AI environments, transforming experimental ideas into production\-ready team assets.
- Own and maintain the organizational AI roadmap for Applied AI Solutions GTM, identifying opportunities to leverage AI technologies for operational excellence.
- Compile and produce the monthly GTM Acceleration MBR, synthesizing data and insights across field activation, partner programs, and business interlocks.
- Serve as technical program manager for cross\-functional projects across GTM Acceleration and Applied AI Solutions, driving execution and stakeholder alignment.
- Partner with GTM leadership to translate business requirements into technical solutions, architecting scalable mechanisms that enhance organizational effectiveness.
- Design and implement field and partner enablement programs leveraging AI technologies, including automated training materials, intelligent workflows, and self\-service tools.
- Identify and automate repeatable work across the GTM organization, using generative AI to eliminate manual processes and accelerate team velocity.
- Drive adoption of AI tools and best practices across the organization, serving as technical advisor and champion for AI\-powered innovation.
About the team
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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- Bachelor's degree in Computer Science, Engineering, or a related technical field
- 5\+ years of technical program management experience managing cross\-functional programs and driving execution across multiple teams
- Hands\-on experience building with generative AI technologies and tools
- Experience with Amazon QuickSight or similar BI/analytics platforms, including building dashboards, data models, and automated workflows
- Demonstrated ability to translate business requirements into technical solutions and drive implementation
- Experience managing rhythm of business processes, including monthly business reviews and cross\-team coordination
PREFERRED QUALIFICATIONS
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- 3\+ years of Go\-To\-Market, Business Development, Sales, or Consulting experience
- Master's degree in a technical field, or Master of Business Administration
- Experience with AWS technologies
- Expert\-level proficiency with QuickSight, including knowledge spaces, flows, Quick Pages, and advanced features
- Hands\-on experience building solutions in Kiro or similar AI development environments
- Demonstrated ability to use generative AI tools as a daily force multiplier for automating repeatable work, performing analysis, and accelerating action
- Experience building and maintaining shared knowledge spaces so that playbooks, accounts, and customer context are discoverable and reusable across teams
- Demonstrated curiosity to prototype in AI tools and turn one\-off ideas into durable team assets
- Experience with AI/ML technologies, including prompt engineering, model selection, and AI application development
- Background in Business Application Technologies, including End User Compute (EUC), Supply Chain, Contact Center as a Service, or related domains
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, Irvine \- 147,900\.00 \- 200,100\.00 USD annually
USA, CA, Mountain View \- 162,700\.00 \- 220,200\.00 USD annually
USA, CA, San Diego \- 147,900\.00 \- 200,100\.00 USD annually
USA, CA, San Francisco \- 162,700\.00 \- 220,200\.00 USD annually
USA, IL, Chicago \- 147,900\.00 \- 200,100\.00 USD annually
USA, NY, New York \- 162,700\.00 \- 220,200\.00 USD annually
USA, TX, Austin \- 147,900\.00 \- 200,100\.00 USD annually
USA, TX, Dallas \- 147,900\.00 \- 200,100\.00 USD annually
USA, WA, Seattle \- 147,900\.00 \- 200,100\.00 USD annually
Salary Context
This $147K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $147K to $200K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Amazon Web Services AI Hiring
Amazon Web Services has 78 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer, Research Scientist, AI Product Manager. Positions span Seattle, WA, US, San Francisco, CA, US, Arlington, VA, US. Compensation range: $177K - $295K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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