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About This Role
Joining Amex Tech means discovering and shaping your contribution to something big. Here, you can work alongside talented technology teams and build a unique career with the Powerful Backing of American Express. With opportunities to work with the latest technologies and a commitment to supporting the broader engineering community, our mission is to power your success. Amex Tech is powered by our technology, our culture, and our colleagues.
The U.S. Consumer Services and Membership Digital Experiences Technology team (USCDT) brings together foundational strategic technology capabilities across digital experience engineering, marketing and advertising technology, enterprise communications, travel and lifestyle, and dining technology. The organization builds customer\-facing capabilities that deepen digital engagement as well as core technology platforms that span business lines and customer segments.
American Express Global Dining brings together world\-class experiences through platforms such as Resy, Tock, and Rooam, creating seamless connections between diners, restaurants, and the broader American Express Membership ecosystem. Our mission is to define the future of hospitality and dining through technology, data, and design.
As part of Team Amex, you’ll have the support, flexibility, and autonomy to make an impact while helping shape the future of how people discover, book, and enjoy dining experiences worldwide.
At American Express, AI is reshaping the future of commerce and redefining the experiences our customers, merchants, and Card Members expect. Within Global Dining, housed within Amex Technology, we are applying AI to reimagine how diners discover and experience restaurants, how restaurants engage and serve their guests, and how our colleagues build and operate the platforms behind those experiences.
Our focus is increasingly on agentic AI: intelligent systems capable of understanding context, reasoning, planning, using tools, and taking actions across complex workflows with appropriate levels of autonomy. These capabilities have the potential to transform dining discovery, reservations, restaurant operations, customer servicing, and the engineering systems that power the Global Dining ecosystem.
We are building these capabilities with the reliability, security, explainability, observability, and governance required to operate AI at American Express scale.
THE ROLE
As a Senior AI Engineer – Agentic AI, Global Dining, you will be a hands\-on engineer responsible for designing, building, integrating, and operating AI\-powered capabilities across the Global Dining ecosystem.
You will develop production AI systems that apply large language models, retrieval, tools, orchestration, machine learning, and generative AI to real\-world customer, restaurant, and platform workflows. You will work across the AI application stack—from LLMs, retrieval and agent orchestration to APIs, data pipelines, evaluations, observability, and production infrastructure.
This is a deeply hands\-on engineering role. You will write production code, prototype emerging approaches, contribute to technical designs, and take AI capabilities from experimentation through deployment, monitoring, and continuous improvement. You will collaborate with engineers across teams to integrate AI models and intelligent services into existing production systems while meeting both technical and business requirements.
As a Senior Engineer, you will provide technical guidance within the team, contribute to engineering standards and reusable patterns, mentor junior engineers, and help foster a culture of engineering excellence and innovation. You will apply advanced expertise in AI engineering while continuing to develop breadth across adjacent technical domains.
You will partner closely with Product, Design, Data, Information Security, Risk, Compliance, and other engineering teams to translate business and customer needs into responsible, scalable AI solutions.
TECHNICAL ENVIRONMENT:
We do not hire against a narrow technology checklist. We are looking for engineers with strong software engineering fundamentals who are excited about solving difficult problems at the intersection of AI, distributed systems, and customer\-facing products.
CORE ENGINEERING STACK:
- Languages: Python, Go, TypeScript
- Cloud and infrastructure: AWS and/or GCP, Kubernetes
- Tools and Platform: LangGraph, LangChain, Docker
- APIs and services: REST, gRPC
- Distributed systems: event\-driven architectures, including Kafka
- Data processing technologies and frameworks for large\-scale data pipelines
AGENTIC AI AND ML:
- Commercial and open\-source LLMs integrated into production applications and agentic workflows
- Tooling for agent orchestration, retrieval\-augmented generation, vector storage, and evaluation
- Prompt engineering, context management, structured generation, and model evaluation
- Machine learning and natural language processing techniques, including Transformer\-based models and their real\-world applications
- Schema validation and structured data handling
- Machine learning operations tooling for model deployment, monitoring, and maintenance
AI\-ASSISTED DEVELOPMENT:
- Use of AI\-assisted and agentic development tools for design, implementation, testing, debugging, and refactoring
- Learning how to apply these tools responsibly while maintaining production\-quality standards
- All systems are built to meet high standards for reliability, security, and auditability, reflecting the responsibility of deploying autonomous AI in a financial services environment
- Bachelor’s degree in Computer Science, Machine Learning, Data Science, Computer Engineering, a related technical field, or equivalent practical experience; advanced degree preferred.
- Significant software engineering experience building and operating production AI/ML systems, including generative AI, LLM applications, or agentic systems.
- Strong proficiency in Python and experience with one or more additional languages such as Go, TypeScript, or Java.
- Strong understanding of modern AI architectures and techniques, including LLMs, NLP, retrieval/RAG, tool calling, structured generation, context management, orchestration, and evaluation.
- Experience integrating commercial or open\-source LLMs into real\-world applications.
- Experience designing scalable, distributed, API\-driven systems and production services, with strong knowledge of reliability, latency, security, observability, cost, and performance optimization.
- Experience building data pipelines and ETL processes using Spark, Hadoop, or comparable modern data\-processing frameworks.
- Experience deploying and operating AI models and pipelines using MLOps, CI/CD, and containerized infrastructure.
- Demonstrated end\-to\-end ownership of production systems, from architecture and implementation through deployment, monitoring, and maintenance.
- Ability to navigate ambiguous technical problems, make pragmatic engineering decisions, and translate emerging AI capabilities into reliable customer experiences.
- Experience mentoring engineers and improving engineering quality through design reviews, code reviews, documentation, and hands\-on collaboration.
- Strong communication and cross\-functional collaboration skills, with the ability to translate business requirements and complex technical concepts for technical and non\-technical audiences.
- Deep expertise in at least one technical domain, with the ability to develop expertise across adjacent areas.
- Strong customer mindset and interest in applying AI to meaningful real\-world experiences.
- Depending on factors such as business unit requirements, the nature of the position, cost and applicable laws, American Express may provide visa sponsorship for certain positions
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 $123K-$215K range is below the median 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 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($169K) sits 21% below the category median. Disclosed range: $123K to $215K.
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.
American Express AI Hiring
American Express has 10 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span New York, NY, US, Phoenix, AZ, US, Sunrise, FL, US. Compensation range: $150K - $215K.
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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