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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
1117 open roles
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Listed pay typically runs
$186,160–$215,000
across 7 roles with salary data.
As an Applied AI/ML Lead within the GT CDAO team, you will tackle business-critical priorities in Cybersecurity, Software, and Technology Infrastructure. You will build and maintain production-ready AI/ML services and pipelines by applying high-quality software engineering practices like modular design, version control, and CI/CD. Your daily work involves developing state-of-the-art models for tasks such as data mining, anomaly detection in time series, text understanding, and recommendation systems. You will utilize a technical stack including TensorFlow, PyTorch, NumPy, Scikit-Learn, and Pandas, while leveraging the LLM ecosystem featuring LangChain, LangGraph, vector databases, RAG, and agentic workflows. This role focuses on solving complex problems within the software development lifecycle by creating large-scale frameworks to accelerate machine learning applications across various business areas through both independent research and collaborative engineering efforts.
Develop state-of-the-art machine learning models for cybersecurity, software, and technology infrastructure.
Build and maintain production-ready AI/ML services using high-quality, modular code and standard software engineering practices.
Implement CI/CD pipelines, model governance, and monitoring systems to ensure reliable deployment in batch and real-time environments.
Develop large-scale frameworks to accelerate the application of machine learning models across various business units.
Create reusable code components and modules for internal and external use.
Research and experiment with new machine learning methods, including LLMs, generative AI, and reinforcement learning.
Design experiments and training frameworks while evaluating performance metrics aligned with specific business goals.
What we're looking for
PhD or MSc in a quantitative discipline such as Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science.
Extensive experience with large language models (LLMs) and the accompanying ecosystem including LangChain, LangGraph, Vector databases, RAG, and Agentic Systems.
Strong AI Engineering skills to design, build, and maintain production-ready machine learning systems using software engineering best practices.
Hands-on experience with machine learning and deep learning methods and toolkits such as TensorFlow, PyTorch, NumPy, Scikit-Learn, and Pandas.
Experience with big data and scalable model training.
Strong written and spoken communication skills to convey technical concepts to both technical and business audiences.
Strong background in Mathematics and Statistics (preferred).
Published research in Machine Learning, Deep Learning, or Reinforcement Learning at a major conference or journal (preferred).