Senior AI Engineer II – Global Commercial Services Technology

🔥 12 hours ago

🌲 North Carolina – Remote

🌲 North Carolina – Remote

💵 $123k - $215.3k / year

⏰ Full Time

🟠 Senior

🤖 AI Engineer

👻 Ghost score 0%

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American Express

WebsiteLinkedInAll Job Openings

10,000+ employees

🏦 Banking

💸 Finance

🤝 B2B

Banking • Finance • B2B

American Express is a global financial services company that issues consumer, business, and corporate credit cards, operates payment and merchant services, and offers banking products such as savings accounts, certificates of deposit, and personal loans. It provides rewards and travel programs, corporate and B2B payment solutions, financial education and security services, and extensive cardmember and merchant support.

📋 Description

• Lead the technical design of new agent capabilities, from ambiguous product intent to shipped system • Build and operate services end to end, from event trigger through LLM reasoning to persisted, surfaced results • Extend and shape the shared agent framework, including orchestration, tool use, structured generation, and observability • Design and tune RAG and embedding pipelines on vector search in the operational database • Design evaluations for new agent behaviors and gate prompt changes on them • Own reliability, including failure classification, idempotency, DLQ handling, and rollout safety for AI features in production • Set standards in design and code review and mentor engineers ramping onto the AI stack • Evaluate emerging models and techniques and integrate effective ones into the platform • Work with TypeScript, Go, Vercel AI SDK, Effect, gRPC, tRPC, Kafka, SQS, Lambda, EKS on AWS, Datadog, feature-flagged rollouts, and infrastructure as code

🎯 Requirements

• 6+ years building large-scale backend or distributed systems in production • Shipped LLM-powered features to real users • Strong TypeScript or Go, with comfort working across both • Strong distributed-systems knowledge: queues, event-driven design, failure modes, and idempotency • Judgment about what an LLM should decide versus what code should decide • 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 (nice to have) • Experience building developer tooling, internal platforms, or frameworks other engineers build on (nice to have) • Experience designing LLM evaluations or operating LLM observability at scale (nice to have) • Experience with durable execution or workflow orchestration engines such as Temporal (nice to have) • AI features shipped in financial services or another regulated industry (nice to have) • Vector search or embedding pipelines in production (nice to have)

🏖️ Benefits

• Bonus • Benefits