Sr AI Engineer I

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.

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.

American Express Technologies is building AI-powered tools to improve how our production systems are monitored, supported, and maintained. This team focuses on applying Generative AI to real operational challenges-like incident triage, root cause analysis, Delivery Pipelines, and workflow automation-at an enterprise scale.

We're looking for an engineer with hands-on experience in Generative AI who wants to build practical, production-ready applications. You'll work on systems that combine LLMs with real-world operational data to reduce manual effort, drive decisions vis data, and improve system availability

At American Express, our culture is built on a 175-year history of innovation, shared values and Leadership Behaviors, and an unwavering commitment to back our customers, communities, and colleagues. From delivering differentiated products to providing world-class customer service, we operate with a strong risk mindset, ensuring we continue to uphold our brand promise of trust, security, and service.

As part of Team Amex, you'll experience our 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.

• 2-5 years of software engineering or data engineering experience
• Hands-on experience building solutions with demonstrated results using LLM-based applications (RAG, chatbots, or similar)
• Proficiency in Python (preferred) or Go
• Understanding of data pipelines and working with structured and unstructured data
• Familiarity with modern AI/ML tooling (e.g., LangChain, Hugging Face, or similar ecosystems)
• Experience deploying applications in cloud or containerized environments (Docker, Kubernetes, etc.)

Preferred Qualifications

• Experience working with vector databases, traditional databases and warehouses, and embedding-based retrieval

• Exposure to observability and/or operational data (logs, metrics, tracing)

• Experience with ITIL data, particularly incident, problem, and change

• Familiarity with APIs, microservices, or full-stack development

• Interest in applying AI to system reliability and operational effectiveness.

• Employment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions.

• Build and iterate on Generative AI applications, including retrieval-augmented generation (RAG) systems and lightweight agent workflows

• Develop data pipelines that bring together logs, metrics, and operational data to support AI-driven use cases

• Prototype quickly and help transition successful ideas into production-ready tools

• Apply LLMs, embeddings, and vector databases to solve problems in incident management and system operations

• Collaborate with engineers and product partners to identify high-impact opportunities for AI in production environments

• Contribute to deployment, monitoring, and ongoing improvement of AI-powered systems

What You'll Learn / Grow Into

• Designing and scaling AI systems in real-world production environments
• Applying GenAI patterns (RAG, agents) to complex and diverse enterprise data
• Building reliable, observable systems-not just prototypes, with measurable effectiveness and outcomes
• Working across teams to turn ideas into adopted mainstream solutions
• Build and iterate on Generative AI applications, including retrieval-augmented generation (RAG) systems and lightweight agent workflows
• Develop data pipelines that bring together logs, metrics, and operational data to support AI-driven use cases
• Prototype quickly and help transition successful ideas into production-ready tools
• Apply LLMs, embeddings, and vector databases to solve problems in incident management and system operations
• Collaborate with engineers and product partners to identify high-impact opportunities for AI in production environments
• Contribute to deployment, monitoring, and ongoing improvement of AI-powered systems

What You'll Learn / Grow Into

• Designing and scaling AI systems in real-world production environments
• Applying GenAI patterns (RAG, agents) to complex and diverse enterprise data
• Building reliable, observable systems-not just prototypes, with measurable effectiveness and outcomes
• Working across teams to turn ideas into adopted mainstream solutions