Senior AI Engineer II – Global Commercial Services Technology

🕒 Setembro 22

🌲 North Carolina – Remoto

infoinfo

💵 $123.000 - $215.250 / ano

⏰ Tempo Integral

🟠 Sênior

🤖 Engenheiro de IA

👻 Score fantasma 1%

infoinfo

🗣️🇺🇸🇬🇧 Inglês obrigatório

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Logo of American Express

American Express

10.000+ funcionários

🏦 Bancário

💸 Finanças

🤝 B2B

Banking • Finance • B2B

A American Express é uma empresa global de serviços financeiros que emite cartões de crédito para consumidores, negócios e empresas, opera serviços de pagamento e de adquirência, e oferece produtos bancários como contas poupança, certificados de depósito e empréstimos pessoais. A empresa fornece programas de recompensas e de viagens, soluções de pagamento corporativo e B2B, educação financeira e serviços de segurança, além de amplo suporte para portadores de cartão e estabelecimentos comerciais.

Descrição

• 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

🎯 Requisitos

• 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)

🏖️ Benefícios

• Bonus • Benefits

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