AI Engineer III

Requirements

• 4+ 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 (Java/Javascript, 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.
• 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.
• Depending on factors such as business unit requirements, the nature of the position, cost and applicable laws, American Express may provide visa sponsorship for certain positions.

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 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.

Core Engineering Stack

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

Agentic AI and ML

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