AI Engineer III

The Enterprise Technology Services organization partners with every part of the American Express business to power the company’s growth and innovation with trust and efficiency, and drive competitive differentiation with speed. We support the delivery and operations of technology, digital, and data capabilities, platforms, and services globally. Specifically, our team is responsible for the company’s technology engineering, architecture, and infrastructure, providing 24x7 support to ensure an uninterrupted, high-quality experience for customers and colleagues. We also provide product management for core enterprise platforms, and lead technology risk and information security, enterprise data governance and platforms, digital product and design, and enterprise AI platforms on behalf of the company.
4+ years of professional software engineering experience.
Some hands-on experience building or contributing to AI-powered features, LLM-based applications, or applied ML systems (professional or project-based).
Solid engineering fundamentals in at least one backend language (Java/Javascript, Python, Go, or TypeScript).
Familiarity with APIs, basic cloud concepts, and modern development practices.
Interest in agentic AI systems, autonomy, and AI-assisted development workflows.
Willingness to learn, take feedback, and grow technical ownership over time.
Comfort working in collaborative, cross-functional teams.
A strong customer mindset and curiosity about real-world problem solving.
Exposure to LLM tooling, prompt engineering, RAG, or agent frameworks through work, coursework, or personal projects.
Internship or early-career experience in fintech or other regulated environments.
Contributions to open-source projects, hackathons, or side projects related to AI or developer tooling.
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.
Contribute to the design and implementation of LLM-powered and agentic product features.
Build and extend agentic AI workflows that reason over context, call tools, and perform actions under guidance from senior engineers.
Help implement and maintain retrieval-augmented generation (RAG) pipelines over financial data, with an emphasis on correctness and safety.
Contribute to shared AI infrastructure such as LLM services, orchestration components, and evaluation or monitoring tooling.
Participate in operating AI systems in production, including monitoring, debugging, and improving reliability and performance.
Collaborate closely with product and design partners, learning to translate customer needs into technical solutions.
Core Engineering Stack
Languages: Python, Go, TypeScript, Java/Javascript.
Cloud and infrastructure: AWS and/or GCP, Kubernetes.
APIs and services: REST, gRPC.
Distributed systems: event-driven architectures, including Kafka.
Commercial and open-source LLMs integrated into agentic workflows.
Tooling for agent orchestration, retrieval-augmented generation, vector storage, and evaluation
Schema validation and structured data handling 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.
Agentic AI and ML
Commercial and open-source LLMs integrated into agentic workflows.
Tooling for agent orchestration, retrieval-augmented generation, vector storage, and evaluation
Schema validation and structured data handling 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.

Work arrangement
Hybrid