Quant Modeling Lead - Vice President

JPMorgan Chase

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Work type
On-site
Location
Jersey City, NJ
Employment
Full-time
Posted
7 days ago
Freshness
Confirmed live today

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How this pay compares to similar roles

Similar $183k
$126k $223k
below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $157,200–$208,668.

Based on 240 similar postings.

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1226 open roles on FindRole.

Listed pay typically runs $156,750–$215,000 across 5 roles with salary data.

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At a glance

TL;DR · Quant Modeling Lead - Vice President

The Quant Modeling Lead - Vice President joins the Controls Room Team within the Control Management organization. In this role, you will serve as a subject matter expert on agentic AI solutions, designing and building dynamic multi-agent orchestration frameworks to strengthen control monitoring, analytics, and decision support. You will research, prototype, and productionize autonomous systems capable of complex reasoning, planning, and tool use to solve risk management problems. Key responsibilities include developing coordination mechanisms, communication protocols, and consensus algorithms for distributed agent systems. You will utilize technologies including LangGraph, AutoGen, CrewAI, LangChain Agents, and Large Language Models as reasoning engines. Your work involves implementing state-of-the-art techniques in reinforcement learning, multi-agent reinforcement learning, and retrieval-augmented generation to provide actionable risk-based intelligence across the firm.

What you'll do

  • Design and architect dynamic multi-agent orchestration frameworks to solve complex control and risk management problems.
  • Research, prototype, and productionize innovative agentic solutions using reinforcement learning and autonomous decision-making systems.
  • Develop coordination mechanisms including communication protocols, task allocation strategies, and consensus algorithms for distributed agents.
  • Build and deploy production-grade multi-agent systems that integrate with strategic control management platforms.
  • Lead the development of autonomous agents capable of complex reasoning, planning, tool use, and memory management.
  • Architect agentic AI services using modern frameworks like LangGraph, AutoGen, or CrewAI within enterprise environments.
  • Design comprehensive evaluation frameworks for multi-agent systems using intrinsic and extrinsic metrics aligned with business goals.
  • Research data to formulate high-impact business questions that can be addressed through agentic AI approaches.

What we're looking for

  • Master's degree or Ph.D. in Computer Science, Artificial Intelligence, or a related technical field.
  • Minimum of 7+ years of AI/ML experience.
  • At least 5 years of experience specializing in multi-agent systems, agentic AI architecture, and autonomous agent development.
  • Deep expertise in multi-agent orchestration frameworks, communication protocols, task decomposition, and collaborative problem-solving.
  • Proven track record of designing and deploying production-scale agentic solutions with autonomous reasoning and planning capabilities.
  • Hands-on experience with frameworks such as LangGraph, LangChain Agents, AutoGen, CrewAI, or custom multi-agent orchestration systems.
  • Extensive experience with Large Language Models as reasoning engines, including prompt engineering, function calling, and tool use.
  • Ability to design comprehensive evaluation frameworks for multi-agent systems using intrinsic and extrinsic metrics.
  • Excellent communication skills to convey complex technical concepts to non-specialists and stakeholders.
  • Ph.D. in Computer Science, Artificial Intelligence, or a related technical field (preferred).
  • Experience with agent memory systems, safety, alignment, governance, graph-based workflows, and RAG (preferred).

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