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Based on 240 similar postings.
Employer
About Nubank
Nubank is one of the world's largest digital financial services companies, offering credit cards, personal loans, savings accounts, and insurance to tens of millions of customers across Brazil, Mexico, and Colombia.
As a Senior AI and Agentic AI Risk Management Specialist at Nubank in Miami or Washington DC, you will join the Model Risk team to build and enhance frameworks for managing risks associated with advanced AI systems. Your daily tasks include conducting independent assessments of generative AI and agentic workflows, identifying model limitations and emerging risks, and designing platform-level controls to ensure safe deployment. You will use Python, SQL, and modern ML tools, along with expertise in LLMs and RAG systems, to evaluate AI quality and behavior. This role requires hands-on experience in AI/ML development or validation, strong technical skills, and the ability to influence first-line teams on platform architecture and embedded controls for AI governance. With a focus on large-scale financial platforms, you will work in a multicultural environment to champion AI risk management as a strategic enabler of safe innovation.
Build and enhance risk management frameworks for AI and Agentic AI systems.
Perform independent assessments of AI systems to ensure quality, behavior, and robustness.
Influence first-line teams on platform architecture and embedded controls for safe deployment.
Develop Key Risk Indicators (KRIs) and metrics for continuous monitoring of AI risks.
Serve as a trusted advisor across the AI/ML lifecycle, evaluating new use cases for governance requirements.
What we're looking for
Hands-on experience developing or validating AI/ML models and systems.
Proficiency in Python, SQL, modern ML tooling, 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 for model risk governance.
Strong technical foundations with a bachelor's or master's degree in a quantitative field such as computer science or data science.
Ability to influence first-line teams on platform architecture and embedded controls for safe AI deployment and monitoring.
Advanced communication skills to explain AI risk to both technical and non-technical stakeholders.
Python
Java
API integration
LLM application engineering
AI agent development
Workflow automation
Evaluation and testing of AI systems
SQL
Debugging and troubleshooting
Prompt and tool orchestration
Structured outputs and validation