Risk Management - Model Risk Program Associate

Join JPMorgan Chase's Risk Management and Compliance team, where your expertise will drive the re-engineering of how model risk is managed across the firm. As part of the Model Risk Governance and Review (MRGR) AI Center of Excellence (AI COE) team, your work will be instrumental in transforming the model risk lifecycle and enabling a new paradigm for how risk professionals and model developers collaborate. As a Risk Management - Model Risk Program Associate in the Model Risk Governance and Review (MRGR) AI Center of Excellence (AI COE) team, you will design and build AI-native tools, workflows, and platforms that re-engineer how model risk professionals conduct validation and governance. You will also bring deep applied AI expertise to the review of AI/ML and LLM-based models deployed across the firm. This role places you at the forefront of how Generative AI is being operationalized in financial services. Job Responsibilities Design, build, and deploy AI and LLM-based solutions that transform core MRGR processes and workflows during validation and governance. Work closely with MRGR teams to identify opportunities for AI to enhance and modernize model risk management practices and to understand the unique challenges and requirements of model risk governance. Remain current with emerging AI and LLM developments, get hands-on with new capabilities to understand their strengths and limitations, assess how they can be applied within MRGR workflows, and communicate actionable recommendations to stakeholders. Enable MRGR teams to effectively leverage AI tools and platforms in their day-to-day workflows, ensuring adoption is aligned with evolving best practices across the organization. Conduct independent model validation and governance activities to mitigate model risk, with a focus on AI/ML models, LLM-based applications, and Generative AI systems. Required Qualifications, Capabilities, and Skills Master's or PhD degree in a quantitative discipline such as Mathematics, Statistics, Computer Science, Engineering, Economics, Finance, or a related field, with strong quantitative and analytical skills. Hands-on experience with applied AI/ML and LLM technologies, including prompt engineering, RAG architectures, agentic AI systems, context engineering, agent skills, MCP architecture, agentic harness, LLM evaluation and beyond. Strong foundation in statistics, econometrics, and machine learning techniques, with a deep understanding of model assumptions, limitations, explainability, and performance evaluation. Familiarity with LLM application tooling and frameworks (e.g., Claude Code, GitHub Copilot, LangChain, vector databases, embedding models, orchestration layers) and an understanding of how these components integrate in end-to-end deployed systems. Passion to stay at the forefront of how Generative AI is being operationalized in financial services and a desire to be a hands-on builder of AI solutions. Strong communication skills with the ability to present complex AI concepts to both technical and non-technical audiences. A risk and control mindset with the ability to ask incisive questions, assess the materiality of model issues, and escalate appropriately