below marketmost similar roles pay hereabove market
This listing doesn't post a salary. Most similar roles pay
$157,200–$245,625.
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
1226 open roles
on FindRole.
Listed pay typically runs
$156,750–$215,000
across 5 roles with salary data.
As an Applied AI/ML - Vice President within the Corporate and Investment Bank Technology team, you will lead the analysis of complex business problems and design state-of-the-art models to support Wholesale Lending Services. You will develop robust machine learning and deep learning solutions, including NLP, personalization, and recommendation systems. Your daily work involves building end-to-end ML, AutoML, and AutoNLP pipelines for tasks like Document Q&A and Information Retrieval. You will collaborate with data engineers and software engineers to productionize models using Python, Java, C/C++, and toolkits like Transformers, Hugging Face, TensorFlow, PyTorch, NumPy, Scikit-Learn, and Pandas. You will also leverage AWS tools such as Sagemaker, EC2, and Glue to deploy scalable solutions and manage LLMs like GPT, Claude, and Llama.
Lead large AI/ML initiatives by defining problem statements and execution roadmaps for product leadership.
Develop state-of-the-art machine learning models for NLP, personalization, and recommendation systems.
Build end-to-end ML, AutoML, and AutoNLP pipelines to operationalize model orchestration for various use cases.
Construct batch and real-time model prediction pipelines with existing application and front-end integrations.
Design large-scale data modeling experiments and explain complex technical concepts to senior stakeholders.
Productionize models by collaborating with software engineers and business partners to ensure client satisfaction.
Deploy scalable AI solutions using AWS tools like SageMaker, EC2, and Glue.
What we're looking for
Master's or Ph.D. in Computer Science, Data Science, Statistics, Mathematical Sciences, or Machine Learning with a strong background in Mathematics and Statistics.
7+ years of experience applying data science and ML techniques to solve business problems using languages like Python, Java, or C/C++.
1+ year of experience working with Generative AI solutions and LLMs such as GPT, Claude, or Llama.
Solid background in NLP, Generative AI, and hands-on experience with Machine Learning and Deep Learning methods.
Extensive experience with ML/Deep Learning toolkits including Transformers, Hugging Face, TensorFlow, PyTorch, NumPy, Scikit-Learn, and Pandas.
Experience building and deploying ML models on AWS using tools like SageMaker, EC2, and Glue.
Experience with Big Data, scalable model training, and the ability to communicate technical concepts to both technical and business audiences.
Published research in Machine Learning, Deep Learning, or Reinforcement Learning at a major conference or journal (preferred).
Experience with A/B experimentation and data/metric-driven product development (preferred).
Ability to develop and debug production-quality code with familiarity with continuous integration and unit test development (preferred).
Machine Learning
Artificial Intelligence
Python
R
Scala
SQL
Spark
SparkML
Hadoop
Hive
AWS
Azure
Google Cloud Platform
NLP
Time Series
Optimization
Operations Research
Statistics
GenAI
LLM
Python
SQL
AWS
Spark
TensorFlow
PyTorch
Scikit-learn
R
Java
RAG
Agentic AI
Transformer
OpenAI API
NLP
Deep Learning
Reinforcement Learning
Time Series
GenAI
LLM
Python
Spark
AWS
SQL
TensorFlow
PyTorch
Scikit-learn
OpenAI API
R
Java
RAG
Agentic AI
Transformer
Deep Learning
NLP
Statistics
Data Wrangling
Python
PyTorch
TensorFlow
Large Language Models
Generative AI
AWS
SageMaker
EKS
Deep Learning
RAG
Reinforcement Learning
Distributed Training
Prompt Engineering
Multi-agent Orchestration
Java
C++
GPU Optimization
Search/Ranking
Recommender Systems
Graph Techniques