Applied AI/ML - Vice President

Our Consumer & Community Banking Group depends on innovators like you to serve consumers, small businesses, municipalities and non-profits.  You’ll support the delivery of award winning tools and services that cover everything from personal and small business banking as well as lending, mortgages, credit cards, payments, auto finance and investment advice. This group is also focused on developing and delivering cutting edged mobile applications, digital experiences and next generation banking technology solutions to better serve our clients and customers.

This is a unique opportunity to apply your skills and leadership in a dynamic environment, directly impacting the future of Home Lending through innovative AI/ML solutions. You will be at the forefront of technology, shaping the next generation of intelligent products and services at JPMorgan Chase.

As Applied AI ML Lead at Consumer & Community Banking Tech, you will drive ML and GenAI projects, leveraging expertise to deliver innovative solutions.

Job responsibilities 

• Work with product managers, data scientists, ML engineers, and other stakeholders to understand requirements. 
• Design, develop, and deploy state-of-the-art AI/ML/GenAI solutions to meet business objectives. 
• Architect and implement robust, cloud-native MLOps/LLMOps pipelines and distributed AI/ML infrastructure (AWS, Azure, GCP) for scalable, efficient deployment and monitoring of models in production.
• Direct the development and deployment of advanced generative AI solutions (LLMs, RAG, NLP, AI Agents) and classical ML models, integrating state-of-the-art techniques into the ML platform to create innovative fintech products.
• Develop advanced monitoring and management tools to ensure high reliability and scalability of AI/ML systems.
• Develop and maintain automated pipelines for model deployment, ensuring scalability, reliability, and efficiency. 
• Implement monitoring mechanisms to track model performance in real-time and ensure model reliability. 
• Communicate AI/ML capabilities and results to both technical and non-technical audiences. 
• Build AI Agents and chatbot
• Stay informed about the latest trends and advancements in the latest AI/ML research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality. 

Required qualifications, capabilities, and skills 

• Bachelor’s degree or MS or PhD in quantitative discipline, e.g. Computer Science, Mathematics, Operations Research, Data Science. 
• 5+ years of experience in Machine Learning and Artificial Intelligence engineering.
• Experience in applied AI/ML engineering, with a track record of developing and deploying business critical machine learning models in production. 
• Proficiency in programming languages like Python for model development, experimentation, and integration with OpenAI API. 
• Extensive hands-on technical experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, AWS Bedrock, Transformers, LangChain/LngGraph. 
• Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), orchestration tools (Airflow, FastAPI, etc.) and architectural design, implementation, and performance optimization. 
• Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, deep learning, reinforcement learning), and generative model architectures. 
• Expert in Large Language models (OpenAI, Anthropic, Mistral, etc) including fine-tuning models, prompt engineering, embeddings and context window.
• Strong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects. 

Preferred qualifications, capabilities, and skills 

• Familiarity with the financial services industries. 
• Expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG). 
• Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies. 
• Familiarity with ethical AI, including bias mitigation, explainability and escalation protocols for risky outputs.