AI Engineer III - Agentic AI

Job d:

Location:

LONDON, United Kingdom; BURGESS HILL, WEST SUSSEX, United Kingdom Job Function:
Engineering & Architecture

Schedule:

Full time Workplace:
Hybrid Career Area:
Technology Company:
American Express Description 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.

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.

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, Type Script Cloud  and infrastructure: AWS and/or GCP, KubernetesAPIs and services: REST, gRPCDistributed 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.

Responsibilities 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…