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About This Role
DESCRIPTION
Do you want to lead a team defining how enterprises build, govern, and operationalize AI at scale? Are you energized by the full spectrum, from the data foundations that fuel AI systems to the training infrastructure, inference optimization, and agentic architectures that put AI into production?
Amazon Web Services (AWS) is looking for a senior technical leader to build and lead a team of specialist Solutions Architects focused on Data & AI within our Strategic Accounts organization. Your team will help customers across the full AI stack: modernizing data architectures and governance for AI readiness, optimizing training and inference infrastructure on accelerators, building semantic layers and context graphs that give AI systems business understanding, and designing agentic systems that operate reliably on governed enterprise data. Depending on organizational needs and your strengths, you may focus your team on data foundations, AI/ML infrastructure, or a combination spanning both.
This is a strategic leadership role. You will set technical direction for your team, architect alongside them on the most complex engagements, coach them through conversations with CAIOs, CTOs, and CDOs, and develop the prescriptive points of view that position AWS as the AI strategy partner for our largest customers. You will shape solutions in spaces that are still taking form and bring customer signals back to AWS service teams to influence roadmaps.
Key job responsibilities
Set technical vision and strategic direction for a team of specialist Solutions Architects spanning data architecture, AI/ML infrastructure, and open source domains
Engage directly with customer CAIOs, CTOs, CDOs, and VPs of Engineering as a strategic advisor on AI readiness, data modernization, inference optimization, and emerging patterns such as semantic layers, context graphs, and agentic architectures
Architect solutions alongside your team for complex, multi-service customer environments spanning data governance, accelerator optimization, training and inference infrastructure, and AI application design
Hire, onboard, and develop senior technical talent; build a diverse and inclusive team culture that attracts and retains top performers
Coach team members on architectural thinking, strategic customer engagement, and executive communication across both data and AI domains
Develop prescriptive architectures, reference implementations, and competitive positioning that helps the broader field SA community lead AI and data conversations with their customers
Represent the team externally through conference presentations, published architectures, and technical keynotes that establish AWS credibility across both the data platform and AI infrastructure space
Partner with AWS service teams (analytics, databases, AI/ML, compute, open source) to shape product roadmaps based on customer signals and emerging trends
Collaborate with peer SA leaders, Professional Services, and specialist organizations to deliver unified solutions across the full AI stack
Drive open source engagement and contribution across data and AI ecosystems where it accelerates customer outcomes and builds community trust
A day in the life
You might start the morning reviewing an inference optimization architecture where a customer is deploying foundation models on Trainium clusters, then join a call with the CAIO of a top-25 enterprise to discuss their strategy for grounding agentic systems in governed enterprise data. After lunch, you coach a team member preparing a customer workshop on migrating from a legacy data warehouse to an AI-ready lakehouse on open formats. Later, you finalize a conference talk on how context graphs and semantic layers are changing the way enterprises feed data to AI agents. You close the day aligning with your peer leaders on a shared customer pursuing end-to-end AI readiness, from data foundations through production agents.
About the team
This role sits within the AWS Strategic Accounts Specialist Solutions Architecture organization. The Data & AI organization works across the full modern AI stack: from lakehouse and streaming architectures, through training and inference infrastructure on accelerators, to the semantic and context layers that make production AI reliable and agentic systems trustworthy. You will own a team charter within this organization, with room to shape its direction as customer needs and the technology landscape evolve.
AWS values diverse experiences. Even if you do not meet all of the qualifications listed in this description, we encourage candidates to apply. If your career is just starting, has not followed a traditional path, or includes alternative experiences, don't let it stop you from applying.
BASIC QUALIFICATIONS
- 7+ years of management of technical, enterprise customer facing resources or equivalent experience
- 10+ years of infrastructure architecture, database architecture and networking experience
- Bachelor's degree
- Experience architecting/operating solutions built on any cloud service provider like Azure, AWS, GCP, OCI, etc.
- Experience architecting, migrating, transforming or modernizing customer requirements to the cloud
- Experience hiring, developing, and managing high-performing technical teams
PREFERRED QUALIFICATIONS
- Experience in people management and CXO level engagement
- Experience in data warehouse technical architectures, data modeling, infrastructure components, ETL/ ELT and reporting/analytic tools and environments, data structures and hands-on SQL coding
- Knowledge of batch and streaming data architectures like Kafka, Kinesis, Flink, Storm, Beam
- Experience working with technical and product stakeholders to define requirements, prioritize features, and influence product roadmaps
- Experience writing technical articles, speaking at technology conferences, and contributing to open source
- Familiarity with AI/ML training and inference infrastructure (accelerator optimization, distributed training, model serving)
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.
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 Diego - 201,000.00 - 272,000.00 USD annually
USA, NY, New York - 221,100.00 - 299,200.00 USD annually
USA, VA, Arlington - 201,000.00 - 272,000.00 USD annually
USA, WA, Seattle - 201,000.00 - 272,000.00 USD annually
Salary Context
This $201K-$299K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($250K) sits 18% above the category median. Disclosed range: $201K to $299K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Amazon Web Services AI Hiring
Amazon Web Services has 79 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Data Scientist, Research Engineer. Positions span New York, NY, US, Bellevue, WA, US, Atlanta, GA, 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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