Job description

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. Within this ecosystem, our team is building a best-in-class Case Management platform to service our customers using modern technology stack. The platform brings together engineering, product, and business teams to deliver scalable, connected capabilities that improve how customer Dispute & Fraud servicing is managed across the enterprise. Artificial Intelligence is a key part of this transformation. From intelligent automation to advanced decision support, we are embedding AI into the Case Management platform while ensuring strong governance, reliability, and responsible use at enterprise scale. If you are interested in a career creating breakthrough software and making an impact on an audience of millions, look no further. As a Sr. AI Engineer within the Case Management Platform team, you will play a key role in advancing AI-driven transformation across Dispute and Fraud Servicing at American Express. You will work at the intersection of data, AI, and platform modernization to build intelligent, scalable solutions that improve customer servicing, increase operational effectiveness, and support strong compliance and governance. In this role, you will develop AI-powered capabilities that bring greater intelligence and automation to dispute and fraud servicing processes, from improving quality and compliance monitoring through automated, insight-driven systems to enabling faster, more informed case handling using connected and enriched enterprise data. You will also help build intelligent systems that unify and enhance servicing data, enabling better decisions and deeper business insights across the platform. Alongside solution development, you will help shape enterprise-grade AI practices by contributing to scalable platforms, standardized approaches, and responsible AI adoption. This includes ensuring that AI solutions are built with strong governance, reliability, transparency, and security, supporting sustainable innovation and long-term business value within the Case Management Platform. • Design and build scalable AI and agentic systems to improve customer engagement and the dispute and fraud servicing experience. • Build intelligent workflows that use context, enterprise data, and tools to support decisions in dispute and fraud servicing. • Design and deliver production-ready AI solutions, including LLM apps, RAG systems, and personalization features, with high accuracy, reliability, and auditability. • Use LLMs, GenAI, or applied machine learning to build and run large-scale AI solutions. • Improve operations with AI-based monitoring, quality checks, and governance through automation and analytics. • Build and support shared AI platforms, including agent orchestration, evaluation systems, and scalable model deployment pipelines. • Own end-to-end delivery, from problem definition and prototyping to deployment, monitoring, and ongoing improvement. • Work closely with product, engineering, and business teams to turn complex problems into effective AI solutions. • Ensure all AI systems meet enterprise standards for security, compliance, explainability, and responsible AI. • 7+ years of experience in software engineering, machine learning, or applied AI, including building production systems. • Experience working with agentic AI systems, including orchestration, tools, memory/state management, and evaluation. • Experience with RAG, vector databases, and the full LLM lifecycle, including prompting, fine-tuning, evaluation, and monitoring. • Strong Python skills and experience building backend services, APIs, and data pipelines. • Hands-on experience with LLMs, GenAI, or applied machine learning in real-world products. • Experience with modern AI/ML frameworks such as LangChain, LangGraph, PyTorch, TensorFlow, or Scikit-learn. • Strong experience with Google Cloud Platform (GCP), including BigQuery, Composer (Airflow), DataProc, and similar services. • Experience designing and deploying scalable cloud-native solutions using modern engineering practices. • Good understanding of distributed systems, event-driven architecture, and large-scale data processing. • Strong problem-solving skills and the ability to turn business needs into technical solutions. • Able to work in a fast-paced, collaborative environment with strong ownership and accountability. Preferred: • Experience with case management platforms and customer data systems. • Experience in regulated industries such as financial services, with knowledge of compliance, governance, and secure AI deployment. • Experience with distributed data technologies and container tools like Docker and Kubernetes. • Familiarity with AI governance, model risk management, and responsible AI. • Contributions to research, patents, or open-source projects in AI/ML or developer tools. • Advanced degree in Computer Science, Machine Learning, AI, or related technical field. • Employment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions.

Requirements

Must-haves

Design and build scalable AI and agentic systems to improve customer engagement and the dispute and fraud servicing experience.
Build intelligent workflows that use context, enterprise data, and tools to support decisions in dispute and fraud servicing.
Design and deliver production-ready AI solutions, including LLM apps, RAG systems, and personalization features, with high accuracy, reliability, and auditability.
Use LLMs, GenAI, or applied machine learning to build and run large-scale AI solutions.
Improve operations with AI-based monitoring, quality checks, and governance through automation and analytics.
Build and support shared AI platforms, including agent orchestration, evaluation systems, and scalable model deployment pipelines.
Own end-to-end delivery, from problem definition and prototyping to deployment, monitoring, and ongoing improvement.
Work closely with product, engineering, and business teams to turn complex problems into effective AI solutions.
Ensure all AI systems meet enterprise standards for security, compliance, explainability, and responsible AI.
7+ years of experience in software engineering, machine learning, or applied AI, including building production systems.
Experience working with agentic AI systems, including orchestration, tools, memory/state management, and evaluation.
Experience with RAG, vector databases, and the full LLM lifecycle, including prompting, fine-tuning, evaluation, and monitoring.
Strong Python skills and experience building backend services, APIs, and data pipelines.
Hands-on experience with LLMs, GenAI, or applied machine learning in real-world products.
Experience with modern AI/ML frameworks such as LangChain, LangGraph, PyTorch, TensorFlow, or Scikit-learn.
Strong experience with Google Cloud Platform (GCP), including BigQuery, Composer (Airflow), DataProc, and similar services.
Experience designing and deploying scalable cloud-native solutions using modern engineering practices.
Good understanding of distributed systems, event-driven architecture, and large-scale data processing.

Nice-to-haves

Experience with case management platforms and customer data systems.
Experience in regulated industries such as financial services, with knowledge of compliance, governance, and secure AI deployment.
Experience with distributed data technologies and container tools like Docker and Kubernetes.
Familiarity with AI governance, model risk management, and responsible AI.
Contributions to research, patents, or open-source projects in AI/ML or developer tools.
Advanced degree in Computer Science, Machine Learning, AI, or related technical field.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmmachine learningpythonmlgcpdockerkubernetes

Platform: oracle-cloud
Effort: medium