AI Engineer - Agentic AI - Future Opportunities

American Express invites you to share your resume so you can be considered for future Agentic AI opportunities in the Enterprise Technology Services organization.

At American Express, our culture is built on a 175-year history of innovation, shared valuesand Leadership Behaviors, and an unwavering commitment to back our customers, communities, and colleagues. 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.

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 Role

As an AI Engineer – Agentic AI, you will be a hands-on builder contributing to the development of production agentic AI systems that operate on real financial data and serve real customers.

You will work alongside experienced engineers, product managers, and designers to design, build, and ship AI-powered features, while learning how to operate within a regulated, customer-facing environment. This role offers strong mentorship and opportunities to grow your technical depth in LLMs, agentic systems, and production AI engineering.

This is not a research-only role. You will write production code, contribute to system design discussions, and help operate what you build after launch, with support and guidance from more senior engineers.

What You’ll Do

• 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.

Technical Environment

We don’t hire to a narrow checklist, but candidates should be excited to grow in a modern, enterprise-scale engineering environment with a focus on agentic AI.

Core engineering stack

• Languages: Python, Go, TypeScript
• Cloud and infrastructure: AWS and/or GCP, Kubernetes
• APIs and services: REST, gRPC
• Distributed systems: event-driven architectures, including Kafka

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.