This role pays more than
53%
of similar roles. Most pay
$177,012–$246,150
— the shaded band above.
At the midpoint, this role pays about
$212k
versus about
$212k
for comparable roles.
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
368 open roles
on FindRole.
Listed pay typically runs
$152,000–$215,000
across 198 roles with salary data.
As the Applied ML and Generative AI Lead at J.P. Morgan, you will serve as a hands-on engineering leader responsible for designing, building, and deploying production-grade machine learning and generative AI services across multiple workstreams. Your daily tasks include setting technical direction, making architecture decisions, mentoring junior engineers, and ensuring solutions meet enterprise standards for security and operational rigor. You will collaborate closely with product managers, data scientists, and other stakeholders to prioritize use cases and develop scalable MLOps capabilities. Key responsibilities also involve fine-tuning generative models for NLP applications, implementing monitoring mechanisms, and communicating AI/ML advancements to both technical and non-technical audiences. The ideal candidate has extensive experience in applied AI/ML engineering, proficiency in Python and frameworks like TensorFlow and PyTorch, and familiarity with cloud computing platforms and microservices design. Knowledge of financial services and expertise in Retrieval-Augmented Generation (RAG) pipelines are also beneficial.
Design and deploy production-grade ML and Generative AI services from concept to deployment.
Optimize generative models for specific NLP use cases, ensuring high-quality text generation and summarization.
Build and institutionalize MLOps capabilities with automated pipelines for model lifecycle management.
Conduct thorough evaluations of generative models and implement improvements to enhance performance in NLP applications.
Mentor junior engineers through design reviews, code reviews, pairing, and coaching to raise engineering quality.
What we're looking for
Bachelor's or Master's degree in Computer Science, Engineering, or a related field with 7+ years of applied AI/ML engineering experience.
Hands-on technical leadership in designing and deploying production-grade ML and Generative AI services.
Proficiency in Python, TensorFlow, PyTorch, Scikit-learn, and OpenAI API for model development and integration.
Experience with cloud computing platforms (AWS, Azure, Google Cloud), containerization technologies (Docker, Kubernetes), and microservices design.
Solid understanding of machine learning fundamentals, including classification, regression, time series analysis, deep learning, reinforcement learning, and generative models like GANs and VAEs.
Ability to optimize and fine-tune generative models for specific NLP use cases, ensuring high-quality outputs in summarization and text generation.
Python
PyTorch
CI/CD
Kubernetes
AWS
Docker
Git
PostgreSQL
Responsible AI
Semantic Modeling
Transformer Architectures
GPU Development
Agentic AI Systems
Version Control
Code Review
Testing
MLOps