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About This Role
DESCRIPTION
Are you a customer-obsessed builder with a passion for helping customers achieve their full potential? Do you have the business savvy, Generative AI background, and sales skills to help position AWS as the cloud provider of choice? Do you love building new, strategic, data-driven businesses from the ground up? Join the Worldwide Specialist Organization (WWSO) Amazon Quick team as the Worldwide GTM Specialist leading the global go-to-market strategy for Managed Knowledge Bases.
The Worldwide Specialist Organization (WWSO) is part of AWS Sales, Marketing, and Global Services (SMGS), which is responsible for driving revenue, adoption, and growth from the largest and fastest-growing small- and mid-market accounts to enterprise-level customers, including public sector. We work backwards from our customers' most complex and business-critical problems to build and execute go-to-market plans that turn AWS ideas into multi-billion-dollar businesses. WWSO teams include business development, specialist, and technical solutions architecture. As part of WWSO, you'll provide expertise across the entire life cycle of an AWS customer initiative — from shaping ideas for new services to accelerating the adoption of established ones. We pride ourselves on thinking big, delivering exceptional results for our customers, and working across AWS as #OneTeam.
Within WWSO, this role owns the worldwide GTM plan for Managed Knowledge Bases — the retrieval and data-grounding layer that makes enterprise Generative AI and agents accurate, trustworthy, and secure. Managed Knowledge Bases connect customers' proprietary content to foundation models through fully managed retrieval-augmented generation (RAG), so the answers customers get are grounded in their own data. You will set direction and standards for how we solve customer problems in this domain, lead specialists, architects, partners, and account teams around the world, and bring the voice of the customer directly into our product roadmap. This is a highly visible, "first-in" role for an emerging, fast-growing business: you see the big picture and set the plan so the world's most customer-obsessed technical teams can make history.
Key job responsibilities
- Own the worldwide GTM strategy for Managed Knowledge Bases — market sizing, target segments and industries, sales plays, pricing/packaging input, and the multi-year adoption plan.
- Build and grow a global pipeline in partnership with field sales, Specialist Solutions Architects, partners, and account teams; help qualify, progress, and close the largest and most strategic opportunities.
- Serve as a domain authority on enterprise retrieval, RAG, vector stores, connectors, and data grounding for GenAI and agentic workloads — translating complex technical trade-offs into clear business outcomes for Line-of-Business and C-suite leaders.
- Define and scale the enablement flywheel: sales playbooks, reference architectures, demos, workshops, and field training that let thousands of AWS sellers and partners land this motion consistently worldwide.
- Represent the voice of the customer to product and engineering teams, converting field signals into a prioritized, data-backed roadmap of features and capabilities.
- Collaborate to build the partner and ecosystem strategy that extends reach and delivery capacity around Managed Knowledge Bases.
- Establish the goals, metrics, and reporting cadence; deliver regular business reviews and recommendations to senior AWS leadership.
- Represent AWS credibly as a thought leader at customer briefings, industry events, and conferences.
A day in the life
You will move fluidly between strategy and execution. In a given week you might define a new industry segment, join an executive briefing to shape a strategic customer's knowledge-base architecture, review pipeline health with a geo leadership team, author a new sales play, unblock a lighthouse proof-of-concept alongside a Specialist SA, and package the field signal from all of it into crisp roadmap input for the product team. You are the connective tissue between customers, the field, partners, and product — setting the standard for how AWS wins in this domain and reporting progress, insights, and trends to leadership.
About the team
The WWSO Amazon Quick team brings AWS's newest and fastest-moving Agentic AI capabilities to market. We are a "first-in" team: when customers need a new way to put their data to work with AI, they count on us to innovate with them, prove what's possible, and then scale it worldwide. We create sales plays, build pipeline, activate partners, and feed customer insight straight into the product roadmap. We think big, earn trust, dive deep, and deliver results as #OneTeam.BASIC QUALIFICATIONS
- 6+ years of Go-To-Market, Business Development, Sales, or Consulting experience
- 5+ years of working with Data & AI related technologies, including, but not limited to, AI/ML (Artificial Intelligence/Machine Learning), GenAI (Generative AI), Analytics, Database, and/or Storage experience
- Experience explaining complex technical concepts to various business and technical audiences
- Experience managing programs across cross functional teams, building processes and coordinating release schedules
- Experience developing strategies that influence leadership decisions at the organizational level
PREFERRED QUALIFICATIONS
- Experience working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage
- Experience driving new business segment or services launches
- Experience engaging and influencing C-level executives, both business and technical
- Experience in written and verbal communication with the ability to present complex technical information in a clear and concise manner to executives and non-technical leaders
- Experience negotiating complex deals with customers and partners or equivalent
- Experience developing and executing sales strategies, tactics, plans, processes, systems and programs
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 - 162,700.00 - 220,200.00 USD annually
USA, CA, Santa Clara - 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, VA, Arlington - 147,900.00 - 200,100.00 USD annually
USA, VA, Herndon - 147,900.00 - 200,100.00 USD annually
USA, WA, Bellevue - 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: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($174K) sits 18% below the category median. Disclosed range: $147K to $200K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Amazon Web Services AI Hiring
Amazon Web Services has 71 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Engineer, Data Scientist. Positions span New York, NY, US, Bellevue, WA, US, Denver, CO, US. Compensation range: $97K - $299K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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