Drimva

JPMorgan Chase

Lead Applied AI ML for Payments

$171K–$260K

Jersey City, NJ +1 · Full-time · Posted Sep 22, 2026

Location
Jersey City, NJ
New York, NY
Experience
6 years of professional AI/ML experience; 4 years of advanced production Python development; 4 years designing and deploying production ML systems on AWS.
Education
Master’s degree in Mathematics, Computer Science, Engineering, or a related quantitative field.

About the job

Leads the design, development, and production operation of AI/ML solutions that improve Wholesale Payments Operations, including document processing and workflow automation.

What you'll do

  • Partner with business stakeholders to define problems, success metrics, and AI/ML roadmaps; lead end-to-end solution delivery.
  • Deploy and operate scalable AI/ML services and data pipelines on AWS, with MLOps, governance, monitoring, and responsible AI practices.
  • Set engineering standards, mentor engineers, and lead code and design reviews.

What they ask for

  • Master’s degree in Mathematics, Computer Science, Engineering, or a related quantitative field.
  • 6 years of professional AI/ML experience delivering production systems.
  • 4 years of advanced production Python development, including use of AI-assisted coding tools; and 4 years designing and deploying production ML systems on AWS.
  • Demonstrated experience building and deploying LLM applications, including retrieval-augmented generation and fine-tuning workflows.

Nice to have

  • Experience in wholesale payments, transaction banking, or financial services.
  • Experience with Kubernetes and infrastructure-as-code, such as Terraform.

Pay

Jersey City,NJ USD $171,000.00-$260,000.00 base salary; New York,NY USD $171,000.00-$260,000.00 base salary

Tools

Python, Amazon Web Services (AWS), SageMaker, Lambda, ECS/EKS, S3, MLflow, Kubeflow

AI focus

Builds production AI/ML solutions for payments operations, including NLP, document AI, OCR, LLM applications, retrieval-augmented generation, and fine-tuning; also establishes MLOps, model governance, monitoring, and responsible AI practices.

Model-written summary · the original posting is authoritative

First seen Oct 28, 2026 · Last verified Nov 5, 2026 (UTC)