AI Engineer II

• Design, build, and ship LLM-powered and agentic product features that change how customers manage their finances.
• Build agentic AI systems that reason over context, invoke tools, take real actions, and recover gracefully from failure.
• Architect and implement production-grade RAG pipelines over sensitive financial data, with strict requirements for correctness, auditability, and safety.
• Contribute to shared AI infrastructure, including LLM services, agent orchestration frameworks, and evaluation and monitoring tooling, that scales agentic development across Amex Technology.
• Own the systems you build in production, including reliability, latency, cost, and failure modes.
• Work closely with product and design partners; engineers in this role are expected to think in terms of customer outcomes, not just technical execution.

Technical Environment

We don’t hire to a narrow checklist, but candidates should be comfortable operating in a modern, enterprise-scale environment with a strong emphasis 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
• Strong schema, validation, and state management practices

AI-assisted development

• Fluency with AI-assisted and agentic development workflows for design, implementation, testing, debugging, and refactoring
• Thoughtful use of these tools while maintaining production-quality engineering 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.

What We’re Looking For

• 5+ years of software engineering experience, including meaningful production experience with LLMs or applied ML systems.
• A track record of shipping AI-powered or agentic systems that real users depend on.
• Strong engineering fundamentals across backend systems, APIs, data pipelines, and cloud infrastructure.
• Hands-on experience with modern LLM tooling and agentic patterns and architectures.
• Fluency with AI-assisted and agentic development workflows.
• Strong sense of ownership and sound technical judgment.
• Comfort operating with ambiguity and turning it into shipped reliable product.
  • A strong product mindset and customer orientati