Machine Learning Engineering & Applied AI ML Lead - Vice President

About the Role

Join us in shaping the future of AI at JPMorganChase, where you can make a real impact by building autonomous agents that solve critical challenges. We value your expertise and encourage you to pioneer new approaches, bridging theory and practice while collaborating with talented teams across the globe to help define how AI transforms the world's largest bank. Experience career growth, mentorship, and the excitement of building next-generation solutions.

We're developing an AI platform and desktop application that helps users automate their document processing workflows at the world's biggest bank, already operating at hundreds of documents per second and doubling every three months, leveraging agentic systems that are powerful, truly generalizable, and scalable while maintaining security in a highly sensitive enterprise environment.

As a Machine Learning Engineer in the Applied Artificial Intelligence and Machine Learning team within Commercial & Investment Banking, you will design and deliver production architectures for AI-powered products and services, working at the intersection of software engineering and scientific research to translate innovative ideas into scalable enterprise solutions. You will collaborate with cloud and SRE teams in a role that offers flexibility for individual contributors and optional management responsibilities, depending on your interests and experience. You will help shape the team culture and drive impactful change.

Job Responsibilities

• Design and deliver enterprise-grade machine learning systems
• Collaborate with cloud and SRE teams to build robust production architectures
• Translate scientific research into scalable ML solutions
• Develop and deploy business-critical, data-intensive applications
• Implement distributed, multi-threaded, and scalable applications
• Build, test, and deploy automated pipelines for ML solutions
• Leverage foundational libraries and services for re-use across teams
• Apply best practices in software engineering and computer science
• Utilize MLOps tools for versioning, reproducibility, and observability
• Align ML problem definitions with business objectives
• Mentor and support team members, with optional management responsibilities

Required Qualifications, Capabilities, And Skills

• Experience in machine learning engineering roles
• Degree in a quantitative discipline (Computer Science, Mathematics, Statistics)
• Proven ability to develop and deploy business-critical, data-intensive applications
• Extensive experience with AWS and Kubernetes
• Proficiency with lower-level libraries such as PyTorch and NumPy
• Hands-on experience implementing distributed, multi-threaded, and scalable applications
• Experience with automated building, testing, and deployment pipelines
• Familiarity with higher-level interfaces like Pydantic AI and Langraph
• Strong understanding of computer science fundamentals and development best practices
• Broad knowledge of MLOps tooling for versioning, reproducibility, and observability
• Ability to understand business objectives and align ML problem definitions

Preferred Qualifications, Capabilities, And Skills

• Experience mentoring or leading teams
• Knowledge of agentic AI concepts
• Experience designing reusable libraries and services
• Interest in bridging scientific theory and enterprise-grade systems
• Passion for innovation and continuous learning