[Hiring] Senior AI Engineer I @American Express

Role Description

As a Senior AI Engineer I – 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.

• Read messy real-world documents.
• Work out what belongs in the record.
• Propose values that fit the customer's policy, so the user reviews instead of types.

Working closely with senior engineers, 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.

Responsibilities

• Build production agentic AI services end to end, from event trigger through LLM reasoning to persisted, surfaced results.
• Contribute to our shared agent framework: orchestration, tool use, structured generation, and observability.
• Build RAG and embedding pipelines on the vector search built into our operational database.
• Implement evals for new agent behaviors with the team, and use them to gate prompt changes.
• Build for reliability: failure classification, idempotency, DLQ handling, and safe rollout of AI features.
• Participate in technical design discussions and code reviews, and grow toward owning designs yourself.
• Help onboard engineers ramping onto the AI stack.

Stack

• 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

Qualifications

• 4+ years building backend or distributed systems in production.
• Shipped LLM-powered features and can walk through how one worked under the hood: prompts, retrieval, output handling, and what surrounded the model call.
• Strong TypeScript or Go, and willingness to work across both.
• Solid distributed-systems fundamentals: queues, event-driven design, failure modes.
• Curiosity about what an LLM should decide versus what code should decide.
• Clear communication across engineering, product, and design.

Nice to Have

• Experience building or maintaining internal platform tooling or frameworks.
• Experience writing LLM evals or working with LLM observability tooling.
• 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.