Senior AI Engineer – Global Commercial Services

🕒 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

• Build production agentic AI services end to end, from event trigger through LLM reasoning to persisted, surfaced results • Contribute to the shared agent framework, including orchestration, tool use, structured generation, and observability • Build RAG and embedding pipelines using vector search in the operational database • Implement evaluations for new agent behaviors and use them to gate prompt changes • Build for reliability through failure classification, idempotency, DLQ handling, and safe AI-feature rollouts • Participate in technical design discussions and code reviews, growing toward owning designs • Help onboard engineers onto the AI stack • Work across TypeScript and Go services, gRPC and tRPC APIs, Kafka, SQS, Lambda, EKS on AWS, feature-flagged rollouts, and infrastructure as code

🎯 Requisitos

• 4+ years building backend or distributed systems in production • Shipped LLM-powered features, including prompts, retrieval, output handling, and surrounding model-call systems • Strong TypeScript or Go, with willingness to work across both • Solid distributed-systems fundamentals, including queues, event-driven design, and failure modes • Curiosity about what an LLM should decide versus what code should decide • Clear communication across engineering, product, and design • Experience building or maintaining internal platform tooling or frameworks (nice to have) • Experience writing LLM evaluations or working with LLM observability tooling (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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