Applied AI/ML - Vice President

JPMorgan Chase & Co.; Wilmington (DE); On-site; USD 180,000 - 240,000; Full time

Job summary

JPMorgan Chase & Co. seeks an Applied AI ML Lead to shape the future of Home Lending through innovative AI/ML solutions.

You will drive ML and GenAI projects and lead the architecture of scalable, cloud-native MLOps pipelines across AWS, Azure, and GCP. You will work with cross-functional teams to deploy generation AI capabilities, monitor model performance in production, and communicate findings to both technical and non-technical audiences.

Qualifications

• Bachelor’s degree or MS/PhD in a quantitative discipline (e.g., CS, Math, OR, Data Science).
• 5+ years of experience in Machine Learning and AI engineering.
• Experience in applied AI/ML engineering with production-grade models.
• Proficiency in Python for model development and OpenAI API integration.
• Extensive hands-on experience with ML frameworks/libraries and APIs (TensorFlow, PyTorch, Scikit-learn, AWS Bedrock, Transformers, LangChain/LngGraph).
• Experience with cloud platforms (AWS/Azure/GCP), containers (Docker/Kubernetes), orchestration tools (Airflow, FastAPI).
• Solid understanding of stats, ML fundamentals, and generative model architectures.
• Expertise in Large Language Models including fine-tuning, prompts, embeddings, context window.
• Strong collaboration to work with cross-functional teams.

Responsibilities

• Collaborate with product managers, data scientists, ML engineers, and stakeholders to understand requirements.
• Design, develop, and deploy state-of-the-art AI/ML/GenAI solutions to meet business objectives.
• Architect and implement cloud-native MLOps/LLMOps pipelines and AI infrastructure for scalable deployment and monitoring.
• Lead development and deployment of generative AI (LLMs, RAG, NLP, AI agents) integrated into fintech platforms.
• Develop monitoring tools to ensure reliability and scalability of AI/ML systems.
• Create automated pipelines for model deployment with scalability and efficiency.
• Implement real-time model performance monitoring and reliability measures.
• Communicate AI/ML capabilities and results to technical and non-technical audiences.
• Build AI agents and chatbots.

Skills

Machine Learning

AI engineering

Python

OpenAI API

TensorFlow

PyTorch

Scikit-learn

LangChain

Cloud computing

Kubernetes

Model deployment

Data science

Education

Bachelor’s degree in quantitative discipline

MS or PhD in quantitative field

Tools

TensorFlow

PyTorch

Scikit-learn

AWS Bedrock

Transformers

LangChain

LngGraph

Docker

Kubernetes

Job description

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.