Applied AI/ML Lead

JPMorgan Chase

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Tampa, FL
Posted
129 days ago
Freshness
Confirmed live yesterday

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How this pay compares to similar roles

Similar $215k
$170k most similar roles pay here $255k

This listing doesn't post a salary. Most similar roles pay $183,477–$246,150.

Based on 240 similar postings.

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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 on FindRole.

Listed pay typically runs $186,160–$215,000 across 7 roles with salary data.

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At a glance

TL;DR · Applied AI/ML Lead

As Applied AI/ML Lead within the Commercial & Investment Bank Healthcare Provider team, you will lead the design, development, and production deployment of AI/ML solutions for image classification, text categorization, and data extraction from scanned TIF documents. You will architect computer vision pipelines using CRNN architectures, integrate OCR technology with generative AI via Amazon Bedrock, and manage scalable training and inference pipelines on AWS SageMaker. The role involves building and managing a team of engineers while implementing MLOps practices like model versioning and drift detection. You will utilize Python, PyTorch, TensorFlow, Hugging Face Transformers, OpenCV, and Pillow for model development, alongside Java or Groovy for microservice integration on AWS EKS. Your work focuses on solving complex document understanding problems by combining visual layout information with textual content to extract structured data from diverse documents.

What you'll do

  • Design, develop, and deploy AI/ML solutions for image classification, text categorization, and data extraction from scanned TIF documents.
  • Architect and implement computer vision pipelines using CRNN architectures for document identification and feature extraction.
  • Fine-tune models for named entity recognition and semantic understanding to extract structured data from complex layouts.
  • Integrate OCR technology and generative AI capabilities via Amazon Bedrock into the document processing pipeline.
  • Build and manage scalable ML training and inference pipelines using AWS SageMaker.
  • Deploy trained models as containerized microservices on AWS EKS for high-throughput production workloads.
  • Establish MLOps practices including model versioning, automated retraining, drift detection, and performance monitoring.
  • Manage labeling strategies and annotation workflows to ensure high-quality ground truth datasets.

What we're looking for

  • Bachelor's degree, MS, or PhD in a quantitative discipline such as Computer Science, Mathematics, Operations Research, or Data Science.
  • 7+ years of experience in applied ML/AI roles with at least 2+ years leading teams or large-scale ML initiatives.
  • Advanced proficiency in Python and enterprise languages including Java or Groovy for backend integration.
  • Expertise in PyTorch, TensorFlow, Hugging Face Transformers, OpenCV, and Pillow for model development and image processing.
  • Deep expertise in computer vision (CRNN architectures) and NLP models for text categorization, named entity recognition, and semantic analysis.
  • Practical experience with OCR technologies and image preprocessing for extracting data from scanned TIF documents.
  • Hands-on experience with AWS SageMaker, Amazon Bedrock, Docker, and Kubernetes for deploying containerized microservices on AWS EKS.
  • Strong knowledge of MLOps practices including MLflow or SageMaker Pipelines for experiment tracking and model lifecycle management.

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