Vice President-Generative AI Lead

We have an exciting opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Applied AI/ML Engineer at JPMorgan Chase within the CT-AWM Risk Technology, you will be an integral part of our AI/ML team, enhancing, building, and delivering trusted market-leading technology products in a secure, stable, and scalable way.

As a Vice President-Generative AI lead within the Corporate Technology-Asset Wealth Management Risk Technology team at JPMorgan Chase, you will play a pivotal role in our AI/ML team, improving, developing, and delivering trusted market-leading technology products in a secure, stable, and scalable manner.

As a Vice President of Applied Artificial Intelligence/Machine Learning Lead in Asset Wealth Management Risk, you will be instrumental in promoting AI/ML technology solutions across various technical domains to support the firm's business goals.

You will work closely with a team of experts to design and architect comprehensive solutions, proactively tackle significant business challenges, and generate valuable insights from data analysis.

Job Responsibilities:


* Design and architect end-to-end solutions in the AI domain, including anomaly detection use cases, data-driven chat applications, and GenAI implementations.


* Develop a deep understanding of key business problems and processes to drive effective solutions.


* Execute tasks throughout the model development process, including data wrangling, analysis, model training, testing, and selection.


* Generate structured insights from data analysis and modeling exercises, presenting them in formats tailored to various audiences.


* Collaborate with data scientists and machine learning engineers to deploy machine learning solutions.


* Conduct ad-hoc and periodic analysis as required by business stakeholders, the model risk function, and other groups.

Required qualifications, capabilities, and skills:


* At least 5 years of relevant experience post-advanced degree (MS, PhD) in a quantitative field (e.g., Data Science, Computer Science, Applied Mathematics, Statistics, Econometrics).


* Experience in statistical inference and experimental design, including probability, linear algebra, and calculus.


* Proficiency in data wrangling, including understanding complex datasets and using Python for cleaning, reshaping, and joining data.


* Practical expertise in both supervised and unsupervised ML projects.


* Strong programming skills in Python, including libraries such as NumPy, pandas, and scikit-learn, as well as R.


* Understanding and usage of the OpenAI API.


* Experience in NLP, including tokenization, embeddings, sentiment analysis, and basic transformers for text-heavy datasets.


* Experience with LLM & Prompt Engineering, including tools like LangChain, LangGraph, and Retrieval-Augmented Generation (RAG).


* Expertise in anomaly detection techniques, algo...