Sr AI Engineer II - Enterprise Architecture

We are looking for engineers who are comfortable operating with autonomy, exercising sound judgment, and pushing the technical envelope within the realities of a regulated financial environment.

- 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 orientation.

- Experience building agentic systems in fintech or other regulated industries. - Experience as a founding engineer or early technical contributor in high-growth environments. - Demonstrated ability to ship technically complex systems in regulated contexts that customers actively rely on. - Meaningful open-source contributions, particularly in AI or developer tooling.

- 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.

- Languages: Python, Go, TypeScript - 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 - Strong schema, validation, and state management practices

- 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.