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About This Role
DESCRIPTION
Are you a customer-obsessed builder with a passion for driving Generative AI and Machine Learning innovation and business impact for ISV customers? Do you love building new strategic and data-driven businesses? Do you want to join one of the fastest-growing organizations within AWS? Join our team as an AWS Generative AI Specialist Seller and unleash the power of Generative AI/ Machine Learning to transform industries and empower customers to achieve their full potential.
As a Generative AI Sales Specialist, you will be at the forefront of driving adoption and revenue growth for AWS's Generative AI services. Your role will involve identifying high-value customer opportunities within a specific territory and engaging with AWS customers to understand their needs and align them with AWS Generative AI solutions. You will leverage your consultative expertise to become a trusted advisor, guiding customers in embedding and deploying AWS Generative AI solutions to unlock new value streams and solve key business problems using AI capabilities. With a passion for developing high-potential opportunities and executing effective strategies, you will own the full-cycle sales engagement plan, from identifying qualified leads to realizing revenue. You will understand a customer’s business initiatives, help develop account plans to achieve those initiatives, identify and drive opportunity win plans to enabling those business initiatives, and ensure successful launch to realize both customer vision and AWS revenue. You will earn trust with Line of Business, Data Science, and IT personas. You will understand detailed business drivers in your forecast, and intentionally work the best opportunities that maximize your ability to hit revenue goals.
You will maintain an in-depth knowledge of AWS's Generative AI services and relevant cross-functional areas to build strong relationships with customers. By driving the adoption of Generative AI technologies, you will play a pivotal role in propelling AWS's revenue growth while helping customers stay ahead of the curve in a rapidly evolving technological landscape. You exercise learn & be curious to articulate AWS’ Gen AI strategy and the services we offer to accelerate these workloads. You conduct compelling executive conversations on the transformational possibilities of Generative AI and Data, while also being capable of helping our customers navigate which services to evaluate for their use-cases. You can describe the “why” and “what” of Generative AI use cases and technical solutions at a 200-level. You will partner with technical resources who help address the “how” and proposed future-state architecture.
Key job responsibilities
- Accelerate customer adoption by defining and implementing technical domain specific sales strategies within your assigned accounts and technology domain. Your strategies will leverage AWS Sales and our partner ecosystem.
- Ideate with Line of Business and C-suite leaders, building trust with your deep technical expertise, and following through to help solve their most compelling business problems.
- Act as the front line within your accounts for all specialist customer engagement in your tech domain.
- Create & articulate compelling value propositions that address specific needs of your customers.
- Build and innovate: Co-Develop sales motions on new product launches and work with product teams on the creation of innovative new services.
- Partner with the world’s biggest system integrators to deliver on customer projects.
- Spearhead market expansion by pinpointing new customer segments and Gen AI use cases
- Collaborate cross-functionally to continuously strengthen AWS's Gen AI value proposition
- Gather voice-of-customer insights to inform product roadmaps and enhance the customer experience
- Drives conversations to build credibility and earn trust with the account teams and customers.
- Drive sales efforts spanning multiple lines of business with decision-making authority and budget ownership.
- Engage systems integrators and closely collaborate with AWS partners to deliver maximum customer value
- Develop and deliver compelling ROI to drive adoption of AWS Gen AI solutions
A day in the life
You’re surrounded by innovation. You’re empowered with a lot of ownership. Your growth is accelerated. The work is challenging. You have a voice here and are encouraged to use it. Your experience and career development are in your hands. We live our leadership principles every day. At Amazon, it's always "Day 1".
About the team
At AWS, we celebrate diversity and foster an inclusive culture where diverse perspectives, backgrounds, and experiences are valued and embraced. We believe that by bringing together talented individuals, we can drive innovation, challenge conventional thinking, and develop solutions that cater to the diverse needs of our global customer base.BASIC QUALIFICATIONS
- 7+ years of full sales cycle, technology sales or equivalent business development, sales engineering/consulting or equivalent experience
- Experience with C-level sales and strategic partnership building
PREFERRED QUALIFICATIONS
- Experience in sales or sales management of infrastructure or cloud technology
- Experience building and managing financial models for business forecasting and problem solving, or experience communicating results to senior leadership
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, restricted stock units (RSUs), and sales incentives. 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 - 157,100.00 - 212,600.00 USD annually
USA, NY, New York - 157,100.00 - 212,600.00 USD annually
Salary Context
This $157K-$212K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($184K) sits 13% below the category median. Disclosed range: $157K to $212K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Amazon Web Services AI Hiring
Amazon Web Services has 72 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Scientist, AI Software Engineer. Positions span New York, NY, US, Jersey City, NJ, US, East Palo Alto, CA, US. Compensation range: $160K - $299K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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