As a Senior AI Engineer II – Agentic AI, you will be a hands-on engineer within Amex Technology, building and evolving production-grade agentic AI systems that power intelligent customer and enterprise experiences. Today that means agents that work in real time to take the tedious part of spend management off our business customers. They read messy real-world documents, work out what belongs in the record, and propose values that already fit the customer's policy, so the user reviews instead of types. Working closely with architects, Product, UX, Data Science, and Engineering teams, you will design, implement, and operate scalable, reliable, and secure AI solutions. You'll work in a small team that owns the full stack of an AI product, from the agent framework and pipelines to the evals and the production service, and you'll drive the direction of major pieces of it.
Global Commercial Services (GCS) serves millions of business customers around the world, from mom-and-pop shops to Fortune 500 companies. We back businesses so they can do more business, with a mission to be the undisputed leader in financial and membership services — responsibly driving double-digit revenue growth. We do that by offering a diverse range of payment and cashflow tools, from a wide range of traditional card products, to working capital and supply chain financing, to new digital solutions that make it easy for our customers to manage a full range of their financial and payment needs.
Why Join Our TeamThe difficult part is not making an LLM demo look convincing. It is building systems that remain useful when context is incomplete, tools fail, models change, and the outcome must be explainable. At American Express, those systems must also meet the security, reliability, privacy, and control standards of a global payments company.
In this role, you will own the technical shape of a problem, not just its implementation. You will lead ambiguous work across services and teams, decide where deterministic software should end and model behavior should begin, establish evaluation and operating standards, and remain accountable after launch. Your decisions will influence both customer-facing capabilities and the shared AI platform other engineers use.
The team also treats developer experience as a product. We build and refine model access, orchestration patterns, evaluation harnesses, observability, guardrails, and AI-assisted engineering workflows. When a tool or practice proves useful, we make it reusable so teams across Amex can move faster without lowering the engineering bar.
ResponsibilitiesWhat You'll DoLead the technical design of new agent capabilities, from ambiguous product intent to shipped system.
Build and operate those services end to end, from event trigger through LLM reasoning to persisted, surfaced results.
Extend and shape our shared agent framework: orchestration, tool use, structured generation, and observability.
Design and tune RAG and embedding pipelines on the vector search built into our operational database.
Design evals for new agent behaviors and gate prompt changes on them.
Own reliability: failure classification, idempotency, DLQ handling, and rollout safety for AI features in production.
Set the standard in design review and code review, and mentor engineers ramping onto the AI stack.
Evaluate emerging models and techniques, and fold the ones that earn their keep into the platform.
AI platform
TypeScript
In-house agent framework on the Vercel AI SDK and Effect
Embeddings and vector search in our operational database
Offline eval harness in Go; live-traffic evals in Datadog
Services and infrastructure
TypeScript and Go services
gRPC and tRPC APIs
Event-driven pipelines on Kafka, SQS, and Lambda
EKS on AWS, feature-flagged rollouts, infrastructure as code
6+ years building large-scale backend or distributed systems in production.
Shipped LLM-powered features to real users, and can talk concretely about what broke and how you found out.
Strong TypeScript or Go, and comfort working across both.
Strong distributed-systems instincts: queues, event-driven design, failure modes, idempotency.
Judgment about what an LLM should decide versus what code should decide.
A track record of driving designs across a team.
Clear communication across engineering, product, and design.
Contributions to open-source projects, especially AI, developer-tooling, or infrastructure libraries.
Experience building developer tooling, internal platforms, or frameworks other engineers build on.
Experience designing LLM evals or operating LLM observability at scale.
Experience with durable execution or workflow orchestration engines such as Temporal.
AI features shipped in financial services or another regulated industry.
Vector search or embedding pipelines in production.
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