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About This Role
DESCRIPTION
Amazon Web Services (AWS) is seeking a Finance Manager to join the Agentic AI Finance team. Agentic AI represents the next frontier of artificial intelligence- autonomous AI systems that can reason, plan, and take action to solve complex problems. The Agentic AI Finance team provides strategic financial leadership for this rapidly scaling portfolio, which includes services such as Amazon Quick Suite, Kiro, AWS DevOps Agent, AWS Security Agent and AWS Transform Custom.
In this role, you will own the end-to-end financial strategy for a service/s within the Agentic AI business, partnering directly with engineering, product, and senior business leaders to drive investment decisions, shape pricing and monetization strategies, and optimize the path from innovation to profitable growth. You will be responsible for building and owning the financial frameworks, planning processes, and controllership mechanisms that enable this fast-moving organization to scale with discipline.
This is a high-visibility, high-impact role that sits at the intersection of finance and transformative technology. You will lead cross-functional initiatives spanning multiple service teams, influence strategic decisions presented to senior leadership (Director and VP level), and establish the financial infrastructure for a business that is defining entirely new market categories. You will think on behalf of the broader Agentic AI organization, inventing and simplifying processes, driving systemic automation, and creating mechanisms that lead to more informed decision-making at scale.
We are looking for a proven finance leader who can operate independently in a highly ambiguous, fast-paced environment. The ideal candidate combines deep financial modeling expertise with strategic thinking, strong cross-organizational influence, and the ability to translate complex AI business dynamics into clear financial narratives. You should be energized by building from the ground up, comfortable challenging assumptions, and passionate about shaping the financial future of agentic AI at Amazon's global scale.
Key job responsibilities
- Own the financial strategy and P&L for a service within the Agentic AI portfolio, including revenue forecasting, cost management, and long-term planning across multiple service lines
- Lead and drive critical financial processes including monthly financial reviews, OP1/OP2 planning cycles, and quarterly forecasts- synthesizing inputs across service teams into a cohesive financial narrative for senior leadership
- Influence strategic decisions at the Director/VP level, including investment prioritization, private pricing deal structures and new service/feature pricings
- Design and implement scalable financial mechanisms, frameworks, and tools that reduce complexity, drive automation, and enable data driven decision making- thinking on behalf of multiple teams to invent and simplify
- Partner cross-functionally with engineering, product, and business leaders to assess the financial implications of new capabilities, evaluate unit economics across different agentic architectures, and develop pricing and monetization strategies for emerging product categories
- Lead deep-dive analyses on complex, ambiguous business challenges- identifying and quantifying risks, opportunities, and growth strategies that inform long-term investment decisions and resource allocation
- Establish and own KPIs, dashboards, and reporting mechanisms that provide visibility into business performance, cost efficiency, and growth trajectory
- Mentor and develop junior team members, contributing to hiring, onboarding, and building a high-performing finance team that can scale with the business
BASIC QUALIFICATIONS
- 5+ years of tax, finance or a related analytical field experience
- Bachelor's degree in finance, accounting, business, economics, or a related analytical field (e.g., engineering, math, computer science), or Bachelor's degree and 5+ years of quantitative role (engineering, process re-engineering, quality assurance) experience
- 5+ years of building financial and operational reports/data sets that inform business decision-making experience
- 5+ years of leading cross-functional initiatives that drive financial performance and strategy experience
- 5+ years of dissecting financial data and identifying patterns that support business strategy experience
PREFERRED QUALIFICATIONS
- 6+ years of solving complex business challenges by delivering accurate and timely financial models, analysis, and recommendations that have a proven impact on business (e.g., financial savings, operational improvements, or customer benefits) experience
- 6+ years of identifying incomplete or inaccurate data, identifying the root cause and creating/implementing an escalation plan experience
- Experience delivering forecasting, budgeting, and variance analysis, and data interpretation of results
- Experience working with large-scale data mining and reporting tools (examples: SQL, MS Access, Essbase, Cognos) and other financial systems (examples: Oracle, SAP, Lawson, JD Edwards)
- Knowledge of SQL/ETL
- MBA, or CPA
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
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, WA, Seattle - 95,400.00 - 163,200.00 USD annually
Salary Context
This $95K-$163K range is in the lower quartile 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.com, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($129K) sits 39% below the category median. Disclosed range: $95K to $163K.
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.com AI Hiring
Amazon.com has 65 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, Data Scientist, AI Product Manager. Positions span Seattle, WA, US, New York, NY, US, Bellevue, WA, US. Compensation range: $129K - $325K.
Location Context
AI roles in Seattle pay a median of $226,100 across 973 tracked positions. That's 6% 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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