AI-Driven Model Risk Governance Associate

Next Frontier Capital; Jersey City (NJ); On-site; USD 180,000 - 250,000; Full time

Job summary

JPMorgan Chases MRGR AI COE team seeks a risk-savvy professional to design and deploy AI-native tools for model risk governance. You will build workflows enabling validation and governance with a focus on AI/LLM systems and generative AI deployments across the firm.

The role requires mastery in AI/ML, LLM architectures, and prompt engineering, plus strong communication to explain complex concepts to both technical and non-technical audiences. Collaboration with MRGR teams is essential.

Qualifications

• Master's or PhD in a quantitative field with strong analytical skills.
• Hands-on experience with AI/ML and LLM technologies, including prompt engineering and RAG architectures.
• Strong foundations in statistics, econometrics, and ML techniques, with explainability and evaluation focus.
• Familiarity with LLM tooling and deployment workflows across platforms.

Responsibilities

• Design, build, and deploy AI and LLM solutions for MRGR processes.
• Collaborate with MRGR teams to modernize model risk governance with AI.
• Stay current with AI/LLM advances and evaluate applicability to MRGR workflows.
• Enable MRGR teams to adopt AI tools in day-to-day governance activities.
• Conduct independent model validation focusing on AI/LLM systems.

Skills

Applied AI/ML

LLM technologies

Prompt engineering

Statistical methods

Communication skills

Education

Master's or PhD

Tools

Claude Code

GitHub Copilot

LangChain

Vector databases

Job description

JPMorgan Chases MRGR AI COE team seeks a risk-savvy professional to design and deploy AI-native tools for model risk governance. You will build workflows enabling validation and governance with a focus on AI/LLM systems and generative AI deployments across the firm.

The role requires mastery in AI/ML, LLM architectures, and prompt engineering, plus strong communication to explain complex concepts to both technical and non-technical audiences. Collaboration with MRGR teams is essential.