Lead AI Engineer — Productivity Systems

Sobre a vaga

About Us Nu is one of the largest digital financial platforms in the world, with more than 122 million customers across Brazil, Mexico, and Colombia. Guided by our mission to fight complexity and empower people, we are redefining financial services in Latin America and this is still just the beginning of the purple future we're building. Listed on the New York Stock Exchange (NYSE: NU), we combine proprietary technology, data intelligence, and an efficient operating model to deliver financial products that are simple, accessible, and human. Our impact has been recognized by global rankings such as Time 100 Companies, Fast Company’s Most Innovative Companies, and Forbes World’s Best Bank. Visit our institutional page https://international.nubank.com.br/careers/ About the Role We are looking for an engineer who has already built with AI in production. You have shipped LLM-powered systems (agents, copilots, RAG pipelines, or AI-driven automations), you know what breaks when they meet real users, and you know how to make them reliable enough for business-critical workflows. Your day-to-day is applied AI engineering: designing agentic workflows, integrating LLMs into internal tools and business processes, building evaluation and guardrail layers, and turning manual, high-friction workflows into AI-assisted ones that thousands of Nubankers depend on. This is not an infrastructure role. You will not spend your days on Terraform, IAM policies, or email deliverability. Cloud fluency helps, but the core of this job is the AI layer — prompts, context, agents, evaluations, integrations — and the product judgment to know where

Habilidades

O que você vai fazer

• Projetar fluxos de trabalho com agentes
• Integrar LLMs em ferramentas internas e processos de negócio
• Construir camadas de avaliação e guardrail
• Transformar fluxos manuais em fluxos assistidos por IA

O que você precisa ter

• Experiência em construir sistemas de IA em produção
• Experiência com sistemas LLM (agentes, copilots, pipelines RAG, automações)
• Conhecimento sobre confiabilidade de sistemas de IA com usuários reais
• Foco na camada de IA (prompts, contexto, agentes, avaliações, integrações)

Perguntas frequentes