Applied Artificial Intelligence/ Machine Learning Lead - Vice President

J.P. Morgan Asset & Wealth Management delivers industry-leading investment management and private banking solutions. Asset Management provides individuals, advisors and institutions with strategies and expertise that span the full spectrum of asset classes through our global network of investment professionals. Wealth Management helps individuals, families and foundations take a more intentional approach to their wealth or finances to better define, focus and realize their goals.​

As an Applied AI/ML Vice President within Global Private Bank, you will lead the design and build of agentic AI systems that execute reliable business workflows end-to-end. You will bring deep expertise in agent architectures—including memory, state, and context management; loop engineering; tool orchestration; and spec-driven development—to deliver safe, observable, and high-quality solutions. You will stay close to cutting-edge research, translating advances in LLMs, agent frameworks, reinforcement learning, knowledge graphs, retrieval, and self-improving systems into practical capabilities. You’ll thrive in a highly collaborative environment, partnering with business, technologists, and control partners to shape requirements, controls, and success metrics.. 

Job Responsibilities:

• Develop advanced agentic AI solutions across NLP, speech analytics, time series, reinforcement learning, and recommendation systems.
• Design robust agent architectures combining LLM reasoning with tools, structured data, and APIs—spanning state, memory, and context management, plus loop engineering (plan/act/observe, verification, termination, and fallback/escalation).
• Engineer reliable agent-driven workflows emphasizing correctness, traceability, and control-aware behavior (guardrails, approvals, auditable decision paths).
• Build knowledge-centric reasoning layers, including knowledge graphs and hybrid retrieval (RAG + graph + structured sources) to improve grounding and accuracy.
• Drive specification-driven development: author specs and contracts (schemas, validators, tool/skill interfaces) and build evaluation/regression harnesses.
• Advance agent quality via recursive self-improvement through automated evaluation and critique loops, red-team feedback, skill/prompt instruction optimization, and outcome-driven dataset curation (human-in-the-loop as needed).
• Coach and mentor AIML team members, setting a high bar for engineering rigor and research depth. 

Required qualifications, capabilities, and skills:

• PhD in a quantitative discipline (e.g., CS/EE/Math/OR/Optimization/Data Science) or equivalent industry/research experience (e.g., 3+ years with PhD-equivalent depth; or MS with 5+ years).
• Demonstrated expertise building agentic AI systems, including several of: memory/state/context management, tool orchestration and workflow reliability patterns, loop engineering, spec-driven development, and prompt/skill instruction optimization.
• Strong hands-on experience with ML/DL methods and toolkits (e.g., PyTorch/TensorFlow plus core Python data/ML stack).
• Ability to design experiments and evaluation frameworks with metrics aligned to business outcomes (quality, reliability, latency, cost, safety).
• Experience with scalable data and model workflows (training and/or inference) and strong software engineering practices.
• strong communication skills to explain technical concepts to both technical and business audiences. 

Preferred qualifications, capabilities, and skills:

• Knowledge in search/ranking, reinforcement learning, or meta-learning (especially for agent routing, policies, and self-improvement).
• Experience with knowledge graphs, entity resolution, and ontology design.

• Experience with A/B experimentation and metric-driven product development; CI pipelines and unit/integration testing.