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About This Role
DESCRIPTION
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Amazon Web Services (AWS) is looking for experienced and motivated customer facing leaders to coach, grow, and partner with technically skilled, customer\-facing Solutions Architects. You will help develop the industry’s best cloud\-based solutions architects by enabling and coaching them on best practices, solution selling, presentation and speaking skills, as well as how to create and present architectures of widely varying size and complexity. In collaboration with sales, you will drive revenue growth across a broad set of customers. If you think you have what it takes to lead the best in the industry, AWS is hiring leader for our Solutions Architects.
As part of this role, you will lead a team of Solutions Architects specialized in Data and AI ISVs, guiding customers as they re\-architect their products around generative AI, machine learning, and modern data platforms. AI is now the filter through which ISVs evaluate architecture decisions — from how they structure their data layer to how they expose intelligence in their product. You and your team will advise customers on RAG pipelines, model selection, fine\-tuning versus prompt engineering trade\-offs, and the data governance foundations that make AI production\-ready. You’ll coach your team to translate these technical choices into the business outcomes — new revenue lines, product differentiation, faster time\-to\-market — , while keeping their own AI skills current.
In this role, you will need to be technically capable and credible in your own right as well as effective in shaping the customer and solutions\-focused skills of your team. You must enjoy learning and introducing new technology in order to help colleagues and customers embrace and adopt new technology. Furthermore, thought leadership in terms of looking beyond the technology and considering the value technology creates for our customers, and helping to change how technology is viewed are important aspects of the role. You will help team members ramp\-up on AWS as well as develop speaking, writing, presentation, and executive interaction skills. You will also need to be adept at interacting, communicating and partnering with other departments within AWS such as our services teams, marketing, and professional services, as well as representing your team to executive management. This is a leader of leaders' role.
Here are some other qualities we are looking for:
Be great fun to work with. At AWS, we have a credo of “Work hard. Have fun. Make history”. In this role, you will love what you do, and instinctively know how to make work fun. You will be dynamic and creative, and willing to take on any challenge and make a big impact.
Enjoy developing technical talent to achieve great things. You will have a passion for educating, training, and enabling cloud computing experts for a diverse and challenging set of Enterprise customers.
Have a strong understanding of solutions innovation. The ideal candidate will have past experience working with customers or similar role and leading large architecture teams.
Key job responsibilities
- As a key member of the business development and sales management teams, ensure success in building and migrating applications, software and services onto the AWS platform
- Hire, on\-board, train, and develop new Solutions Architects from internal and external sources
- Educate enterprise customers on the value proposition of AWS, and participate in architectural discussions to ensure solutions are designed for successful deployment in the cloud
- Coach Solutions Architects in the skills needed to conduct one\-to\-few and one\-to\-many training sessions so they can transfer skills to customers who are considering or using AWS
- Capture and share best\-practice knowledge amongst the AWS solutions architect community
- Guide and motivate the development of whitepapers, data sheets, and other high\-value customer facing guidance and best practices
- Build deep relationships with decision makers within customer accounts to enable them to be “Cloud advocates”
- Act as a conduit and liaison between customers, service engineering teams and support
BASIC QUALIFICATIONS
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- 15\+ years design/implementation/consulting experience of distributed applications
- 10\+ years management of technical, customer facing resources
- Working knowledge of software development tools and methodologies
- Modern cloud platform skills
- Computer Science /relevant degree and/or experience highly desired
PREFERRED QUALIFICATIONS
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- Experience working within software development or Internet\-related industries
- History of successful technical consulting and/or architecture engagements with large\-scale customers or enterprises
- Experience migrating or transforming legacy customer solutions to the cloud
- Familiarity with common enterprise services
- Presentation skills with a high degree of comfort speaking with executives, IT Management, and developers.
- Strong written communication skills
- High level of comfort communicating effectively across internal and external organizations
- AWS Solution Architecture certification or relevant cloud expertise
- Demonstrated ability to adapt to new technologies and learn quickly
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 \- 277,400\.00 \- 350,000\.00 USD annually
USA, NY, New York \- 265,400\.00 \- 350,000\.00 USD annually
USA, VA, Arlington \- 241,200\.00 \- 326,400\.00 USD annually
USA, WA, Seattle \- 241,200\.00 \- 326,400\.00 USD annually
Salary Context
This $277K-$350K range is above the 75th percentile 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($313K) sits 46% above the category median. Disclosed range: $277K to $350K.
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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