Applied Artificial Intelligence/ Machine Learning Lead - Vice President

JPMorgan Chase & Co.; Jersey City (NJ); On-site; USD 250,000 - 450,000; Full time

Job summary

JPMorgan Chase & Co. is seeking an Applied AI/ML Vice President within Global Private Bank to design and build agentic AI systems for end-to-end business workflows.

You will bring deep expertise in agent architectures, memory, state and context management, tool orchestration, and loop engineering to deliver safe, observable, high-quality solutions. You will collaborate with business and technologists to shape requirements, controls, and success metrics, translating advances in LLMs, retrieval,

Qualifications

• PhD in a quantitative discipline or equivalent depth; MS with 5+ years also acceptable.
• Expertise in building agentic AI systems and related patterns.
• Strong experience with ML/DL toolkits and Python stack.

Responsibilities

• Develop advanced agentic AI solutions across NLP, time series, and RL.
• Design agent architectures spanning memory, state, and context management.
• Engineer reliable agent-driven workflows with guardrails and auditable paths.
• Build knowledge-centric reasoning layers with knowledge graphs and retrieval.
• Write specs/contracts (schemas, validators) and build evaluation harnesses.
• Drive self-improvement loops, critique cycles, and data curation for ML systems.
• Mentor AIML team members and raise engineering rigor.

Skills

Agent architectures

Memory/state/context mgmt

Tool orchestration

Loop engineering

Spec-driven development

Prompt/skill instruction

ML/DL methods

PyTorch

TensorFlow

Education

PhD in CS/EE/Math/OR/DS

MS + 5+ years

Tools

PyTorch

TensorFlow

Job description

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.