Controls Room - Quant Modeling Lead - Vice President

JPMorgan Chase & Co.; Jersey City (NJ); On-site; USD 180,000 - 240,000; Full time

Job summary

JPMorgan Chase & Co. seeks a Quant Modeling Lead to design and build agentic AI systems that enhance control monitoring, analytics, and decision support across the firm.

You will lead architecture for multi-agent orchestration and partner with tech teams to deploy scalable, real-time solutions in enterprise environments.

Qualifications

• Master's degree or Ph.D. in Computer Science, AI or related technical field.
• 7+ years AI/ML experience with 5+ years in multi-agent and autonomous agent development.
• Deep expertise in multi-agent orchestration and agent coordination.
• Proven track record deploying production-scale agentic solutions.
• Experience with LangGraph, LangChain Agents, AutoGen, CrewAI or similar tools.

Responsibilities

• Serve as SME on agentic AI solutions and autonomous systems.
• Design and implement multi-agent orchestration frameworks for real-time collaboration.
• Research and productionize agentic solutions using MARL and related techniques.
• Develop agent coordination mechanisms, protocols, and consensus methods.
• Deploy scalable, production-grade multi-agent systems integrated with control platforms.
• Collaborate with Tech partners to deploy agentic AI services in enterprise environments.
• Lead development of autonomous agents capable of planning, tool use, memory management and feedback.

Skills

AI/ML experience

Multi-agent systems

Agentic AI architecture

Autonomous agents

Agent orchestration

LLMs as reasoning engines

Prompt engineering

Communication skills

Education

Master's degree/Ph.D. in CS/AI

Ph.D. in CS/AI (preferred)

Tools

LangGraph

LangChain Agents

AutoGen

CrewAI

Job description

As a Quant Modeling Lead within the Controls Room Team, you will design and build agentic systems that strengthen control monitoring, analytics, and decision support across the Firm. Our mission is to work across agent architecture, workflow orchestration, tool and data integration, and the design of agent reasoning patterns to enable natural, multi-step problem solving. This is a high-impact opportunity within our Control Management organization that comes with great visibility across all groups at the firm and the ability to impact thousands of employees by increasing efficiency in their day to day.

The Controls Room Team within Control Management works collaboratively with other control disciplines and oversees existing control functions as well as the development of new control functions and protocols. The team’s objective is to empower the three lines of defense with risk-based information to manage their control environment. We achieve this by collecting, analyzing and monitoring firmwide risk-based intelligence into all lines of business and corporate functions.

The Controls Room gathers information from key business and technology partners in order to:

• Facilitate better decision making and actionable insights across the Firm.
• Create cost-effective and automated reporting; and
• Provide views into risk and issues.

Job Responsibilities:

• Serve as theSubject Matter Expert (SME)on agentic AI solutions, multi-agent architectures, and autonomous system design, providing strategic guidance and technical leadership across the organization.
• Design, architect, and implementdynamic multi-agent orchestration frameworksthat enable autonomous agents to collaborate, coordinate, and adapt in real-time to solve complex control and risk management problems.
• Research, prototype, and productionizeinnovative agentic solutionsleveraging state-of-the-art techniques in agent-based modeling, reinforcement learning, multi-agent reinforcement learning (MARL), and autonomous decision-making systems.
• Develop sophisticatedagent coordination mechanisms, including communication protocols, task allocation strategies, consensus algorithms, and emergent behavior management for distributed agent systems.
• Build and deployproduction-grade multi-agent systemsthat integrate seamlessly with strategic control management platforms, ensuring scalability, reliability, and real-time performance.
• Collaborate with Tech partners to architectagentic AI servicesusing modern orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI, or custom-built solutions) and deploy them within enterprise environments.
• Lead the development ofautonomous agentscapable of complex reasoning, planning, tool use, memory management, and self-improvement through feedback loops.
• Research data to discover insights and formulate relevant high-impact business questions that can be addressed through agentic AI and multi-agent approaches.
• Stay at the forefront of research in agentic AI, multi-agent systems, and autonomous AI, continuously implementing state-of-the-art methods and models.

Required Qualifications, Capabilities and Skills:

• Master's degree/Ph.D. in Computer Science, Artificial Intelligence or related technical
• Minimum of 7+ years of AI/ML experiencewith at least5 years specializing in multi-agent systems, agentic AI architecture, and autonomous agent development
• Deep expertise in multi-agent orchestration frameworks and dynamic agent coordination, including experience with agent communication protocols, task decomposition, and collaborative problem-solving
• Proven track recordof designing and deployingproduction-scale agentic solutionsthat demonstrate autonomous reasoning, planning, and execution capabilities
• Hands-on experience with agentic AI frameworkssuch as LangGraph, LangChain Agents, AutoGen, CrewAI, or custom multi-agent orchestration systems
• Strong understanding of agent architecturesincluding ReAct, Plan-and-Execute, Reflection, Tree of Thoughts, and other advanced reasoning patterns
• Extensive experience with Large Language Models (LLMs) as reasoning enginesfor agentic systems, including prompt engineering for agent behaviors, function calling, and tool use
• Able to design comprehensive evaluation frameworksfor multi-agent systems, including both intrinsic and extrinsic metrics aligned with business goals
• Able to independently research and propose innovative agentic solutionswith strategic vision for problem-solving
• Excellent communication skillsto work with non-specialists in a partnership model, convey complex technical concepts clearly, and create trust with stakeholders

Preferred Qualifications, Capabilities and Skills:

• Ph.D. in Computer Science, Artificial Intelligence or related technical

• Experience with agent memory systems (short-term, long-term, semantic, episodic)
• Knowledge of agent safety, alignment, and governance frameworks
• Familiarity with graph-based agent workflows and state machines
• Experience with vector databases and retrieval-augmented generation (RAG) for agentic systems