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About This Role
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 this 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.
How will you make an impact in this role?
The Data Analytics Solutions – Emerging Technologies team is seeking a seasoned Senior Engineer with deep expertise in backend engineering and enterprise\-scale data solutions for AI and Data Science. This role focuses on architecting and developing robust, scalable backend APIs and data platforms that enable cutting\-edge AI applications within Technology Risk and Information Security domains.
As a senior technical leader, you will drive the design and implementation of scalable, secure, and highly performant backend services—spanning data pipelines, model serving, and ongoing monitoring. Your influence will extend beyond coding to defining architecture principles, enforcing best practices, and mentoring engineers to deliver highly scalable production\-grade systems aligned with enterprise goals.
Key Responsibilities:
- Architect, design, and lead the development of scalable backend APIs and microservices that support AI/ML workflows, model serving, and inference pipelines at enterprise scale.
- Drive end\-to\-end backend engineering of AI pipelines, model training orchestration, inference services, and data processing frameworks.
- Collaborate closely with data scientists and AI engineers to translate complex AI/ML requirements into performant, maintainable backend systems.
- Lead architectural discussions, conduct code reviews, and provide technical mentorship within the engineering team, promoting clean code, reusable components, and solid design patterns.
- Define and enforce API design standards, security best practices, versioning, authentication/authorization, and operational metrics to ensure backend services are robust, secure, and scalable.
- Integrate AI/ML solutions and APIs with enterprise infrastructure, ensuring seamless integration, governance, and compliance.
- Champion best practices in MLOps, DevSecOps, and responsible AI design, fostering a culture of engineering excellence.
- Stay current with emerging backend technologies, cloud\-native architectures, and AI frameworks to continuously improve platform capabilities.
Required Qualifications:
- Bachelor’s or Master’s degree in computer science, Engineering, or a related technical field.
- 8\+ years of software engineering experience, with 3\+ years in senior or lead backend roles focused on enterprise\-scale systems.
- Strong backend engineering skills including API design, microservices architecture, service orchestration, and distributed systems.
- Proven experience architecting and developing backend platforms and APIs to support AI/ML workloads, including model serving and data pipelines.
- Proficiency in Python and backend frameworks; experience with cloud\-native services (AWS, Azure, GCP), Kubernetes, and Docker.
- Deep understanding of security best practices, API versioning, authentication/authorization, and scalable service design.
- Experience mentoring engineers and driving architectural decisions in cross\-functional teams.
- Familiarity with data infrastructure and streaming technologies such as Kafka, Spark, and databases (SQL/NoSQL/vector DBs).
Preferred Experience:
- Full\-stack development experience, including frontend frameworks and UI integration with backend services, is highly desirable.
- Hands\-on experience with AI/ML frameworks such as TensorFlow, PyTorch, LangChain, Hugging Face, and AutoGen.
- Strong knowledge of MLOps tools like Kubeflow, Argo, MLflow, and model deployment strategies.
- Background in cybersecurity or enterprise risk analytics.
- Experience building observability and monitoring solutions for backend services using Prometheus and ELK stack.
- Comfortable working with RESTful APIs, GraphQL, or gRPC in microservice ecosystems.
Tools \& Technologies:
- Languages: Python, Java (optional), Go (optional)
- Frameworks \& Libraries: Flask, FastAPI, Django, TensorFlow, PyTorch, scikit\-learn
- AI \& GenAI: LangChain, Hugging Face, AutoGen, Google ADK
- API Technologies: GraphQL, gRPC, OpenAPI/Swagger
- Security \& Identity: OAuth2, JWT, Vault
- MLOps \& Orchestration: Kubeflow, Argo Workflows, MLflow, Triton Inference Server
- Data Platforms: Kafka, Spark, NiFi, SQL/NoSQL, pgVector, Milvus
- Cloud \& Containers: AWS, Azure, GCP, Kubernetes, Docker
- Observability: Prometheus, Elasticsearch, Kibana
- CI/CD: Jenkins, GitLab CI, XL Release, GitHub Actions
Salary Range: $123,000\.00 to $215,250\.00 annually \+ bonus \+ benefits
The above represents the expected salary range for this job requisition. Ultimately, in determining your pay, we’ll consider your location, experience, and other job\-related factors.
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
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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, but not limited to, 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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Employment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions.
Salary Context
This $123K-$215K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $123K to $215K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
American Express AI Hiring
American Express has 17 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Software Engineer. Positions span Phoenix, AZ, US, New York, NY, US, US. Compensation range: $124K - $282K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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