AI and Agentic AI Risk Management Senior Specialist

Nubank•Washington, DC

7h•Hybrid

About The Position

Nubank is a leading digital financial platform with over 122 million customers across Brazil, Mexico, and Colombia. We are dedicated to simplifying financial services through technology and data intelligence. The Model Risk team is expanding its capabilities to manage the risks associated with advanced AI, including Generative AI and Agentic AI systems. This role will focus on AI quality, model and agent behavior, and platform controls to ensure the safety and reliability of these systems. This is a senior, hands-on technical position where you will help shape the AI risk management framework at Nubank. You will conduct independent assessments of AI systems, design guardrails, and provide guidance to engineering and AI development teams. The scope includes AI quality, agentic behavior, and platform controls, excluding cybersecurity, data privacy, and fraud.

Requirements

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.
Experience evaluating and testing ML and generative AI systems, including custom evals, benchmarking, stress testing, and drift/degradation monitoring.
Demonstrated experience in risk identification, control definition, and framework building; understanding of model risk governance principles and independent effective challenge.
Experience working with large datasets and building dashboards and analyses to support risk visibility.
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.
Able to engage and align stakeholders across first and second lines of defense as a credible technical peer.
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.

Nice To Haves

Experience in a 2nd or 3rd line of defense.
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).

Responsibilities

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 identify gaps, materiality, and critical risks, and define prioritized mitigation actions.
Assess the effectiveness, proportionality, and sufficiency of first-line monitoring for AI systems.
Perform independent technical assessments of 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 such as drift, instability, fairness, and robustness concerns.
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, evaluations, analyses, and playbooks (including AI-enabled automation) to improve the speed, scale, and effectiveness of AI governance and validation.
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 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.