Lead AI Engineer — Productivity Systems

h2 strong About Us /strong /h2 p 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. /p p 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. /p p 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 nbsp; a href= https://international.nubank.com.br/careers/ https://international.nubank.com.br/careers/ /a nbsp; nbsp; /p h2 strong About the Role /strong /h2 p class= font-claude-response-body break-words whitespace-normal 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. /p p class= font-claude-response-body break-words whitespace-normal 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. /p p class= font-claude-response-body break-words whitespace-normal 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 AI genuinely helps versus where deterministic automation is the right answer. /p h3 strong Key Responsibilities /strong /h3 h4 strong Applied AI amp; Agentic Systems /strong /h4 ul li Design, build, and ship LLM-powered agents and workflows that automate complex internal processes end-to-end. /li li Work hands-on with frontier models and the modern AI stack: tool/function calling, structured outputs, MCP, RAG, multi-agent orchestration. /li li Own the full lifecycle of an AI system: from problem discovery and prototype to production hardening, monitoring, and iteration. /li /ul h4 strong Evaluation amp; Reliability /strong /h4 ul li Build evaluation harnesses, guardrails, and quality feedback loops so AI systems can be trusted in production — not just demoed. /li li Define what good looks like for non-deterministic systems and instrument it: evals, regression suites, human-in-the-loop review where it matters. /li /ul h4 strong Intelligent Workflow Automation /strong /h4 ul li Use orchestration platforms (e.g., n8n) and custom integrations as delivery vehicles for AI-in-the-loop automation across business units. /li li Integrate enterprise platforms (Slack, Google Workspace, Jira, internal APIs) into coherent, AI-assisted workflows. /li /ul h4 strong AI Adoption amp; Governance /strong /h4 ul li Drive the technical strategy for AI adoption within engineering and business workflows. /li li Develop governance frameworks that make AI coding assistants and agents safe, compliant, and effective — balancing developer freedom with security and operational risk. /li /ul h4 strong Multiplier Work /strong /h4 ul li ul li Create Golden Paths, reference implementations, and documentation that let other teams build AI workflows safely on their own. /li li strong For Lead/IC6: /strong act as the technical reference for applied AI in the domain, influence architecture beyond the immediate team, mentor senior engineers, and partner with ITSec and Privacy to align AI solutions with company policy. /li l