Applied AI/ML Lead

JPMorgan Chase & Co.•Chicago, IL

7h•$137,750 - $235,000

About The Position

As an Applied AI/ML Lead within the Commercial & Investment Bank Technology team at JPMorganChase, you will play a crucial role in analyzing business problems, experimenting with state-of-the-art models, and developing machine learning and deep learning solutions. You will use your knowledge of ML toolkit and algorithms to deliver the right solution. You will be a part of an innovative team, working closely with our product owners, data engineers, and software engineers to build new systems. We are looking for someone with a passion for data, ML, and Software Development, who can understand the data landscape in large and complex organizations.

Requirements

BS or MS or PhD in Computer Science or Data Science or Statistics or Mathematical sciences or Machine Learning. Strong background in Mathematics and Statistics.
7+ years’ experience in applying data science, ML techniques to solve business problems and one of the programming languages like Python, Java, C/C++, etc.
Experience with LLMs and Prompt Engineering techniques.
1+ year of experience working with Gen AI solutions / LLMs such as GPT, Claude, Llama etc.
Solid background in NLP, Generative AI and hands-on experience and solid understanding of Machine Learning and Deep Learning methods and familiar with large language models
Extensive experience with Machine Learning and Deep Learning toolkits (e.g.: Transformers, Hugging Face, TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals.
Experience with Big Data and scalable model training and solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences.
Experience with building and deploying ML models on AWS esp. using AWS tools like Sagemaker, EC2, Glue, etc.
Have good understanding about the Active Learning, Agent/Multi Agent Learning, Learning from Supervision/Feedback, etc. Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments.
Ability to work on tasks and projects through to completion with limited supervision. Passion for detail and follow through. Excellent communication skills and team player

Nice To Haves

Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journals
Experience with A/B experimentation and data/metric-driven product development
Ability to develop and debug production-quality code and familiarity with continuous integration models and unit test developmentled later

Responsibilities

Lead programs and provide directions to successfully implement the large AI/ML initiatives and assist product leadership in defining the problem statements, execution roadmap
Develop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as NLP, personalization, or recommendation systems and collaborate with business, operations, and other technology colleagues to understand AI needs and devise possible solutions.
Develop end-to-end ML/AutoML/AutoNLP pipelines and operationalize the end-to-end orchestration of the ML models to support the various use cases like Document Q&A, Search, Information Retrieval, classification, personalization, etc.
Build both batch and real-time model prediction pipelines with existing application and front-end integrations.
Collaborate to develop large-scale data modeling experiments, explain complex concepts to senior leaders and stakeholders.
Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production as well as work with Product Owners and Software Engineers to productionize the models and Partners closely with business partners to identify impactful projects, influence key decisions with data, and ensure client satisfaction.
Deliver regular team updates and maintain full transparency into the team's priorities, progress, and deliverables across stakeholders and champion a culture of recognition by actively celebrating individual and team accomplishments, reinforcing a positive and high-performing team environment.