AI and Agentic AI Risk Management Senior Specialist

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/  

AI Risk Framework & Governance

• Build and continuously enhance the risk management framework for AI and Agentic AI systems, including inventory standards, assessment methodologies, control design, and issue management.
• Inventory and map Nubank's AI use cases to surface gaps, materiality, and the most critical risks, and define prioritized mitigation actions.
• Assess whether first-line monitoring is effective, proportionate to model risk, and sufficient to keep AI systems fit for purpose over time.

Independent AI Assessment

• Perform independent technical assessments across generative AI, and agentic workflows, covering data, assumptions, methodology, testing, behavior, and monitoring.
• Assess risks in LLM-powered applications, including RAG pipelines, tool use, autonomy boundaries, model/agent quality, human oversight, and hallucination risk.
• Identify and document model limitations, failure modes, and emerging AI risks, including drift, instability, fairness, and robustness concerns.

Controls, Platform & Enablement

• Influence first-line teams on platform architecture and embedded controls for the safe deployment and monitoring of AI.
• Build Key Risk Indicators (KRIs) and metrics for continuous monitoring of AI risk.
• Develop tools, evals, analyses, and playbooks (including AI-enabled automation) to improve the speed, scale, and effectiveness of AI governance and validation.

Advisory & Advocacy

• Serve as a trusted advisor across the AI/ML lifecycle, evaluating new use cases for materiality and governance requirements prior to deployment.
• Discuss and report AI risk status and independent opinions to stakeholders, including senior managers and, where relevant, regulators.
• Champion AI risk management as a strategic enabler of safe and scalable AI adoption, and build AI risk literacy across engineering, product, and risk teams.
• Work in a multicultural, diverse, and highly skilled environment.

• Education: A bachelor's or master's degree in a quantitative field (computer science, data science, statistics, mathematics, engineering, or related).
• Hands-on AI/ML experience: A track record developing or validating AI/ML models and systems, ideally a candidate who has moved from an AI / Machine Learning Engineer background into model risk, governance, or risk management. You don't need to have trained foundation models from scratch, but you need solid, current technical depth.
• Strong technical foundations: Proficiency in Python, SQL, and modern ML tooling; familiarity with LLMs, RAG systems, prompt engineering, and AI agent frameworks.
• Evaluation and testing: Experience evaluating and testing ML and generative AI systems, including custom evals, benchmarking, stress testing, and drift/degradation monitoring.
• Risk management experience: Demonstrated experience in risk identification, control definition, and framework building; understanding of model risk governance principles and independent effective challenge.
• Data skills: Experience working with large datasets and building dashboards and analyses to support risk visibility.
• High agency and adaptability: Comfortable operating in ambiguity, synthesizing fragmented technical and business context into a clear view of how complex AI systems actually work, and making sound judgments without a complete playbook.
• Influencing skills: Able to engage and align stakeholders across first and second lines of defense as a credible technical peer.
• Communication: Strong written and verbal skills, you can explain AI risk to a data scientist and to a regulator, and use different language for each.
• Advanced or fluent English: You will meet with partners and stakeholders across countries and prepare documentation and presentations in English.
• PLUS: Experience in a 2nd or 3rd line of defense.
• PLUS: Familiarity with regulatory Model Risk Management and AI frameworks (e.g., SR 11-7 / SR 26-2 / OCC 2011-12, NIST AI RMF, EU AI Act).

Hybrid 2-3 times/week: Our hybrid work model brings us to the office at least twice a week, on strategic days designed to maximize team connection and collaboration.
For more details, visit https://building.nubank.com/nu-hybrid-work-model/