JPMorgan Applied AI/ML Lead - Vice President 

United States, New Jersey, Jersey City 

459320924

As an Applied AI/ML Lead in our Technology team, 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.

Job Responsibilities

• Lead programs and provide directions to successfully implement the large AI/ML initiatives
• 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.
• 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 funders and stakeholders.
• Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production.
• 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.

Required qualifications, capabilities, and skills.

• 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.
• At least 7 years’ experience in one of the programming languages like Python, Java, C/C++, etc.
• At least 7 years’ experience in applying data science, ML techniques to solve business problems.
• Experience with LLMs and Prompt Engineering techniques.
• 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

Preferred qualifications, capabilities and skills

• 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. Familiarity with continuous integration models and unit test development