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About This Role
Why Join American Express
Joining Amex Tech means discovering and shaping your contribution to something big. Here, you can work alongside talented tech teams and build a unique career with the Powerful Backing of American Express. With a range of opportunities to work with the latest technologies, and a commitment to back the broader engineering community through open source, our mission is to power your success. Because Amex Tech is powered by our technology, our culture, and our colleagues.
The Technology organization enables and accelerates the company’s growth strategies, delivering global capabilities and services in support of Amex’s customers and colleagues, while maintaining 24/7 servicing and availability to ensure uninterrupted, high-quality customer experience. Technology provides the foundation for everything we do in the company while driving differentiation through building and leveraging innovative technology and data insights.
The Enterprise AI Platform organization partners with business and technology teams to shape the Enterprise AI strategy, bring emerging technologies to the forefront, and build scalable, reusable AI platforms. This team plays a pivotal role in enabling American Express to operate as an AI-powered enterprise by equipping teams with the skills, platforms, and seamless deployment capabilities needed to accelerate responsible AI innovation, enhance decision-making, and drive customer impact with speed and scale.
Join a team shaping the future of AI at American Express. You will work directly with business and product leaders to turn high-value opportunities into trusted, secure, and responsible AI solutions, while helping successful ideas become reusable capabilities across the enterprise.
FDE partners across American Express to transform high-value business opportunities into trusted, secure, and scalable AI solutions delivering measurable outcomes, accelerating adoption, and turning successful innovations into reusable enterprise capabilities.
Role Summary
As a Forward Deployed AI Engineer, you will partner closely with business units, product teams, and engineering organizations to identify and reframe high-value opportunities, translate business challenges into clear value propositions, and build working solutions using Generative AI, agentic systems, and intelligent automation.
You will take opportunities from discovery and demonstration through proof of value, production scaling, adoption, and transition to long-term ownership. Demonstrations are a means of validating grounded value propositions, not the end goal. You will be accountable for advancing credible opportunities into real adoption and durable ways of working, with each stage tied to measurable outcomes and balanced with enterprise requirements for security, reliability, and responsible AI.
This is a hands-on engineering role with a significant stakeholder engagement component. You will influence without direct authority, communicate across levels of seniority and technical familiarity, and work alongside engineers to build and deploy reliable software.
You will challenge assumptions about how work is performed, analyze processes as systems, and identify the technical, operational, and organizational levers that can improve them. You will define a longer-term direction while sequencing practical near-term solutions that build toward it.
Candidates may come from mobile, web, backend, distributed-systems, or workflow-platform backgrounds. What matters most is the ability to understand a system end to end, form a point of view, engage deeply with users, and turn direction into working software
- 3 or more years of experience delivering software, technical solutions, or digital products in an engineering, architecture, or technical consulting capacity.
- Demonstrated ability to take an ambiguous business or user problem through discovery, implementation, and measurable validation.
- Experience using coding agents in a structured engineering workflow, including specification-driven development, context management, iterative implementation, and reusable knowledge repositories.
- Software engineering fluency and the ability to contribute directly to applications, integrations, services, or technical prototypes.
- Experience in at least one relevant domain, such as mobile, web, backend services, APIs, distributed systems, platform engineering, workflow orchestration, or cloud-native development.
- Ability to reason about systems end to end, identify constraints and leverage points, and connect technical decisions to business outcomes.
- Strong communication and stakeholder-partnership skills, including the ability to explain technical concepts, challenge existing approaches constructively, and influence without direct authority.
- Ability to define a longer-term direction and sequence incremental solutions that build toward it.
- Bachelor’s degree in computer science, engineering, or a related field, or equivalent practical experience.
Employment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions. *
Preferred Qualifications
- Experience building or deploying solutions powered by language models, agentic systems, or AI-enabled process flows.
- Experience with retrieval-augmented generation, vector databases, tool calling, model context protocols, knowledge retrieval, or workflow technologies such as Temporal, LangGraph, LangChain, or LlamaIndex.
- Experience designing human-in-the-loop controls and operational safeguards, with knowledge of AI evaluation, observability, and responsible AI.
- Experience supporting technology adoption or operating-model transformation, particularly in financial services or another regulated industry.
Success Measures
Success in this role will be measured by:
- AI opportunities that progress from discovery to proof of value and, where appropriate, production adoption.
- Measurable business outcomes tied to clearly defined KPIs.
- Ability to identify and reframe valuable opportunities while connecting near-term delivery to longer-term transformation.
- Solutions that improve the broader workflow and are adopted into sustainable operating models.
- Strong stakeholder relationships, technical quality, and effective management of security, risk, and reliability.
Reusable capabilities and implementation patterns incorporated into the enterprise AI platform. *
Working Model and Travel
Occasional travel may be required for workshops or key delivery milestones, but frequent travel is not expected. Roles may be based near selected American Express offices to support close partnerships with regional business and product teams.
At American Express, our culture is built on a 175-year history of innovation, shared values and Leadership Behaviors, and an unwavering commitment to back our customers, communities, and colleagues. From delivering differentiated products to providing world-class customer service, we operate with a strong risk mindset, ensuring we continue to uphold our brand promise of trust, security, and service.
As part of Team Amex, you’ll experience our powerful backing with comprehensive support for your holistic well-being and many opportunities to learn new skills, develop as a leader, and grow your career. Here, your voice and ideas matter, your work makes an impact, and together, you will help us define the future of American Express.
We back you with benefits that support your holistic well-being so you can be and deliver your best. This means caring for you and your loved ones' physical, financial, and mental health, as well as providing the flexibility you need to thrive personally and professionally:
- Competitive base salaries
- Bonus incentives
- 6% Company Match on retirement savings plan
- Free financial coaching and financial well-being support
- Comprehensive medical, dental, vision, life insurance, and disability benefits
- Flexible working model with hybrid, onsite or virtual arrangements depending on role and business need
- 20+ weeks paid parental leave for all parents, regardless of gender, offered for pregnancy, adoption or surrogacy
- Free access to global on-site wellness centers staffed with nurses and doctors (depending on location)
- Free and confidential counseling support through our Healthy Minds program
- Career development and training opportunities
For a full list of Team Amex benefits, visit our Colleague Benefits Site .
American Express is an equal opportunity employer and makes employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, age, or any other status protected by law. American Express will consider for employment all qualified applicants, including those with arrest or conviction records, in accordance with the requirements of applicable state and local laws, including the California Fair Chance Act, the Los Angeles County Fair Chance Ordinance for Employers, and the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance. For positions covered by federal and/or state banking regulations, American Express will comply with such regulations as it relates to the consideration of applicants with criminal convictions.
We back our colleagues with the support they need to thrive, professionally and personally. That's why we have Amex Flex, our enterprise working model that provides greater flexibility to colleagues while ensuring we preserve the important aspects of our unique in-person culture. Depending on role and business needs, colleagues will either work onsite, in a hybrid model (combination of in-office and virtual days) or fully virtually.
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Salary Context
This $103K-$174K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At American Express, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($139K) sits 34% below the category median. Disclosed range: $103K to $174K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
American Express AI Hiring
American Express has 42 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Phoenix, AZ, Sunrise, FL, New York, NY, US. Compensation range: $150K - $256K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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