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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 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 an 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.
At American Express, AI is reshaping the future of commerce and redefining the experiences our commercial customers and card members expect. Within Amex Technology, we are building platforms, products, and governance that enable agentic AI systems to operate responsibly and at scale across the enterprise.
Our focus is on agentic AI development: designing intelligent, adaptive systems that can plan, reason, and act across complex workflows with appropriate levels of autonomy. These systems power autonomous workflows, decision support, and customer-facing experiences—while meeting the high standards for security, explainability, reliability, and compliance required in financial services.
We partner closely with product, design, and business teams to deliver agentic capabilities that reduce operational friction, improve decision-making, and transform how customers interact, transact, and grow.
The Enterprise Data & AI Team creates the platforms, products, and governance that power enterprise AI. Our team enables rapid experimentation, seamless deployment, and responsible operation of AI systems at scale — delivering trusted, compliant, and high-performance AI solutions that drive meaningful business outcomes, partnering with stakeholders to support use cases from inception to implementation.
American Express is building an enterprise-grade Agentic AI Platform designed to enable teams to safely build, deploy, operate, discover, govern, and continuously improve AI agents at scale. We are looking for a Senior AI Engineer to help design and build the foundational platform capabilities that power agentic AI across the enterprise. This is a hands-on engineering role at the intersection of AI systems, distributed platforms, cloud infrastructure, developer tooling, and enterprise governance . This role is ideal for an engineer who wants to go beyond building individual AI applications and instead build the platform and infrastructure that enables hundreds of teams and AI agents to operate safely and reliably at enterprise scale . What You'll Build
You will contribute to the architecture and implementation of capabilities across the Agentic AI Platform, including: Agent Runtime & ExecutionDesign and build scalable agent runtime infrastructure using Kubernetes and cloud-native technologies. Develop runtime abstractions that allow agents to execute across internally managed sandboxed environments and third-party AI platforms. Build capabilities for agent orchestration, tool execution, state and context management, memory, asynchronous workloads, event-driven execution, and multi-agent workflows. Enterprise Agent Control PlaneBuild a unified control plane for managing agents across heterogeneous execution environments and AI providers. Develop APIs and services for agent registration, configuration, deployment, versioning, lifecycle management, policy enforcement, and runtime management. Create abstractions that reduce provider-specific complexity while preserving access to differentiated capabilities across AI ecosystems. Agent Governance & SecurityEngineer platform-level controls for enterprise AI governance, including agent identity, authentication and authorization, tool permissions, policy enforcement, data boundaries, auditability, and lifecycle controls. Build mechanisms for governing models, prompts, tools, MCP servers, knowledge sources, agent-to-agent interactions, and external integrations. Partner with security, risk, privacy, and governance teams to translate enterprise requirements into scalable technical controls. Agent Discovery & EcosystemBuild registries and catalogs that allow developers and AI systems to discover reusable agents, tools, skills, prompts, models, knowledge sources, and other platform capabilities. Develop metadata, ownership, versioning, dependency, certification, and discovery mechanisms that enable a healthy enterprise agent ecosystem. Agent ObservabilityBuild end-to-end telemetry for agent execution, including traces, events, model interactions, tool calls, latency, token consumption, cost, failures, policy decisions, and quality signals. Develop capabilities that make complex and multi-agent workflows explainable and debuggable. Enable platform and application teams to understand agent behavior across multiple models, runtimes, tools, and external systems. Evaluation & Continuous LearningDevelop evaluation frameworks for measuring agent quality, reliability, safety, and task performance. Build automated evaluation pipelines incorporating offline evaluations, production signals, human feedback, regression testing, and experimentation. Help create continuous learning loops that turn production telemetry and feedback into actionable improvements to agents, prompts, tools, models, and platform capabilities. Developer ExperienceBuild SDKs, APIs, CLIs, templates, local development environments, and self-service workflows that make agent development simple and productive. Create opinionated paved roads that allow developers to move quickly while automatically incorporating enterprise security, governance, observability, and operational standards. AI CI/CD & Platform EngineeringBuild CI/CD capabilities specifically designed for AI agents, including automated evaluation, policy validation, security checks, artifact/version management, deployment, promotion, rollback, and release controls. Design GitOps and infrastructure-as-code patterns for deploying and managing agent workloads. Help establish engineering standards for taking agents from experimentation to production safely and repeatedly.
Minimum Qualifications
- Strong software engineering experience building production systems using languages such as Python, Java, Go, or TypeScript .
- Strong understanding of Generative AI, LLMs, agent architectures, tool/function calling, retrieval, context management, and agent orchestration .
- Experience building production applications or platforms using major model providers or AI platforms.
- Experience with Kubernetes, containers, microservices, distributed systems, and cloud-native architecture .
- Experience designing production APIs and event-driven or asynchronous systems.
- Strong understanding of modern cloud infrastructure and infrastructure-as-code practices.
- Experience with CI/CD, automated testing, production observability, and software delivery practices.
- Strong understanding of security fundamentals including identity, authentication, authorization, secrets, and least-privilege access.
- Ability to navigate ambiguous technical problems and turn emerging technologies into reliable production systems.
- Strong communication skills and ability to collaborate across engineering, architecture, product, security, and governance organizations.
- Candidates are required to have a bachelor’s degree
Preferred Qualifications
Experience in several of the following areas is highly desirable:
- Agent frameworks and orchestration technologies such as LangGraph, Semantic Kernel, Google ADK, OpenAI Agents SDK, or similar frameworks .
- Model Context Protocol (MCP) and emerging agent interoperability protocols like A2A.
- Multi-agent systems and agent-to-agent communication patterns.
- Kubernetes operators, controllers, service meshes, or sophisticated Kubernetes platform engineering.
- Agent/model gateways and intelligent model routing.
- Vector databases, retrieval systems, embeddings, and enterprise knowledge architectures.
- LLM and agent evaluation frameworks, LLM-as-judge techniques, human feedback systems, experimentation, and quality measurement.
- AI observability, distributed tracing, OpenTelemetry , Langfuse and production monitoring.
- Experience with Evals
- Policy engines and policy-as-code.
- AI security, prompt injection defenses, tool security, data-loss prevention, and AI-specific threat modeling.
- Platform engineering, internal developer platforms, developer portals, and enterprise service catalogs.
- Large-scale distributed systems operating in highly regulated environments.
What Success Looks Like
You will help make it possible for developers across American Express to move from an agent idea to a governed production deployment through a consistent, self-service platform. Successful platform capabilities will make the secure and reliable path the easiest path: developers should be able to build once and operate agents across multiple AI ecosystems while receiving governance, identity, observability, evaluation, deployment, and operational capabilities by default. You will play a key role in establishing the engineering foundations for an enterprise ecosystem in which AI agents can be built, discovered, trusted, governed, observed, evaluated, deployed, and continuously improved at scale .
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.
Salary Context
This $123K-$215K range is below the median 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 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 $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 ($169K) sits 20% below the category median. Disclosed range: $123K to $215K.
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.
American Express AI Hiring
American Express has 34 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Safety. Positions span New York, NY, Sunrise, FL, US, New York, NY, US. Compensation range: $124K - $282K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 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.
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