Controls Room - Quant Modeling Lead - Vice President

JPMorgan Chase & Co.•Jersey City, NJ

7h•$147,250 - $215,000

About The Position

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.

Requirements

Master's degree/Ph.D. in Computer Science, Artificial Intelligence or related technical
Minimum of 7+ years of AI/ML experience with at least 5 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 record of designing and deploying production-scale agentic solutions that demonstrate autonomous reasoning, planning, and execution capabilities
Hands-on experience with agentic AI frameworks such as LangGraph, LangChain Agents, AutoGen, CrewAI, or custom multi-agent orchestration systems
Strong understanding of agent architectures including ReAct, Plan-and-Execute, Reflection, Tree of Thoughts, and other advanced reasoning patterns
Extensive experience with Large Language Models (LLMs) as reasoning engines for agentic systems, including prompt engineering for agent behaviors, function calling, and tool use
Able to design comprehensive evaluation frameworks for multi-agent systems, including both intrinsic and extrinsic metrics aligned with business goals
Able to independently research and propose innovative agentic solutions with strategic vision for problem-solving
Excellent communication skills to work with non-specialists in a partnership model, convey complex technical concepts clearly, and create trust with stakeholders

Nice To Haves

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

Responsibilities

Serve as the Subject 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 implement dynamic multi-agent orchestration frameworks that enable autonomous agents to collaborate, coordinate, and adapt in real-time to solve complex control and risk management problems.
Research, prototype, and productionize innovative agentic solutions leveraging state-of-the-art techniques in agent-based modeling, reinforcement learning, multi-agent reinforcement learning (MARL), and autonomous decision-making systems.
Develop sophisticated agent coordination mechanisms, including communication protocols, task allocation strategies, consensus algorithms, and emergent behavior management for distributed agent systems.
Build and deploy production-grade multi-agent systems that integrate seamlessly with strategic control management platforms, ensuring scalability, reliability, and real-time performance.
Collaborate with Tech partners to architect agentic AI services using modern orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI, or custom-built solutions) and deploy them within enterprise environments.
Lead the development of autonomous agents capable 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.