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This listing doesn't post a salary. Most similar roles pay
$161,250–$240,437.
Based on 240 similar postings.
Employer
About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has
1226 open roles
on FindRole.
Listed pay typically runs
$156,750–$215,000
across 5 roles with salary data.
As an Applied Artificial Intelligence/ Machine Learning Lead - Vice President within Global Private Bank, you will lead the design and construction of agentic AI systems that execute reliable business workflows end-to-end. You will build robust agent architectures incorporating memory, state, and context management, loop engineering, and tool orchestration to deliver safe, observable solutions. Your daily work involves developing advanced solutions across NLP, speech analytics, time series, reinforcement learning, and recommendation systems. You will utilize PyTorch, TensorFlow, and the core Python data stack to engineer knowledge-centric reasoning layers using knowledge graphs and hybrid retrieval. You will drive specification-driven development by authoring schemas and building evaluation harnesses. This role solves complex problems regarding correctness, traceability, and control-aware behavior through recursive self-improvement and automated critique loops.
Design and build agentic AI systems that execute reliable end-to-end business workflows.
Architect agent systems incorporating memory, state, context management, and loop engineering.
Engineer workflows with built-in guardrails, traceability, and auditable decision paths.
Build knowledge-centric reasoning layers using knowledge graphs and hybrid retrieval methods.
Author technical specifications, schemas, and contracts for spec-driven development.
Implement recursive self-improvement through automated evaluation, critique loops, and red-teaming.
Build evaluation and regression harnesses to measure quality, reliability, and cost.
Coach and mentor AIML team members on engineering rigor and research depth.
What we're looking for
PhD in a quantitative discipline 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 memory/state management, tool orchestration, loop engineering, and spec-driven development.
Strong hands-on experience with ML/DL methods and toolkits such as PyTorch, TensorFlow, and the core Python data/ML stack.
Experience with scalable data and model workflows for training and/or inference and strong software engineering practices.
Ability to design experiments and evaluation frameworks with metrics aligned to business outcomes like quality, reliability, latency, cost, and safety.
Strong communication skills to explain technical concepts to both technical and business audiences.
Knowledge in search/ranking, reinforcement learning, or meta-learning (preferred).
Experience with knowledge graphs, entity resolution, ontology design, A/B experimentation, and CI pipelines (preferred).
GenAI
LLM
Python
SQL
AWS
Spark
TensorFlow
PyTorch
Scikit-learn
R
Java
RAG
Agentic AI
Transformer
OpenAI API
NLP
Deep Learning
Reinforcement Learning
Time Series
Machine Learning
Deep Learning
Generative AI
LLMs
NLP
Python
Java
C++
PyTorch
TensorFlow
Hugging Face
Transformers
NumPy
Scikit-Learn
Pandas
AWS
SageMaker
EC2
Glue
Prompt Engineering
Big Data
GenAI
LLM
Python
Spark
AWS
SQL
TensorFlow
PyTorch
Scikit-learn
OpenAI API
R
Java
RAG
Agentic AI
Transformer
Deep Learning
NLP
Statistics
Data Wrangling