AI Engineer - Enterprise Architecture

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 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.

Responsibilities

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.

Qualifications

What We’re Looking For

• 2+ 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 (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.

Preferred Qualifications

• 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.