Applied AI ML Lead - Agentic AI

JPMorgan Chase & Co., one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

As an Engineering lead at JPMorgan Chase within the AIML Platform team, you will be leading a team of software engineers and driving the vision, architecture, and engineering standard for an enterprise multi agent system. This role combines deep technical leadership in Agentic AI with people skills, driving both the strategic vision and hands-on work with team.

Job responsibilities:

• Lead the design, development, and evolution of Enterprise AI systems, taking it from vision to implementation, enabling production-ready AI capabilities.

• Architect enterprise-grade Agentic AI platform including multi-agent orchestration, tool/function integration, memory + RAG/grounding, guardrails, observability, and evaluation.

• Lead and manage a team of software engineers responsible for developing Multi Agent Systems.

• Partner with stakeholders to define requirements, navigate tradeoffs, and ship multi-quarter initiatives that move core company metrics.

• Create frameworks and guardrails that enable fast, safe iteration on agent behavior, evaluation, and rollout.

• Embrace and drive a culture of accountability for customer and business outcomes.

Required Qualifications, Capabilities and Skills:

• 5+ years of experience in software engineering and 2+ years in leading software engineering teams.

• Proven experience in architecting AI agents or autonomous workflows, not just apps that consuming AI APIs.

• Expertise in model evaluation, agent orchestration, reasoning frameworks, evaluation.

• Proficiency in agent orchestration frameworks (e.g., LangGraph/AutoGen/CrewAI) including multi-agent coordination, state management, and tool/function integration.

• Have strong technical depth and Architect mindset that enables you to guide architecture, debug complex failures, and make trade-offs for efficiency.

Preferred Qualifications, Capabilities and Skills

• Experience architecting and implementing Multi Agent Systems in production.

• Extensive experience as a data scientist, AI/ML engineer, or AI/ML researcher.