What are the responsibilities and job description for the Applied AI & ML Lead - Markets Operations position at JPMorgan Chase?
Bring your expertise in applied artificial intelligence and machine learning to a team improving how Markets Operations runs at scale. You will partner across operations, product, engineering, and data to deliver production-grade solutions with measurable impact. In a collaborative environment, you will shape strategy, mentor talent, and build durable capabilities designed for reliability, control, and real-world adoption.
As an Applied Artificial Intelligence and Machine Learning Lead at JPMorganChase within Markets Operations in the Commercial & Investment Bank, you will drive the strategy, design, and delivery of solutions that improve operational efficiency, resilience, and control outcomes. You will lead a team of scientists and engineers and collaborate with senior stakeholders to prioritize high-impact opportunities and deliver solutions that scale. You will set technical direction, strengthen engineering and governance practices, and translate complex concepts into clear, outcome-focused results.
Job Responsibilities:
Lead the end-to-end delivery of machine learning and generative AI solutions that measurably improve Markets Operations outcomes
Set technical direction and execution strategy across model development, deployment, and adoption aligned to business priorities
Oversee the architecture and production deployment of AI applications, including agent-based and workflow-automation solutions
Manage, coach, and develop a team of scientists and engineers, fostering a collaborative and inclusive culture of continuous learning
Establish and enforce best practices for model monitoring, evaluation, and performance optimization in production environments
Partner with business, operations, and technology leaders to shape problem statements, success metrics, and delivery roadmaps
Guide analysis of large, complex datasets to identify drivers, risks, and automation opportunities
Ensure solutions are engineered for reliability, scalability, and maintainability, with strong operational readiness
Communicate technical approaches, decisions, and results through clear documentation and cross-functional forums
Required Qualifications, Capabilities, and Skills:
- Formal training or certification on applied artificial intelligence and machine learning concepts and 5 years applied experience
- Bachelor’s or Master’s degree in computer science, data science, artificial intelligence, or a related field (or equivalent experience)
- Demonstrated leadership delivering AI/ML initiatives from concept through production, including team and stakeholder management
- Strong applied experience in machine learning, including feature engineering, model development, and statistical analysis on large datasets
- Hands-on proficiency in Python and common machine learning libraries (for example, scikit-learn, TensorFlow, or PyTorch)
- Proven experience deploying, operating, and maintaining production machine learning systems, including incident and performance ownership
- Working knowledge of machine learning operations practices (machine learning lifecycle management, automation, and reproducibility)
- Experience building and deploying generative AI applications and evaluating large language model outputs for quality and risk
- Strong communication skills, including the ability to translate technical concepts for senior stakeholders and non-technical partners
Preferred Qualifications, Capabilities, and Skills:
- Doctorate in a quantitative field or equivalent advanced applied research experience
- Experience delivering AI/ML solutions in financial services, capital markets, or operations-focused environments
- Experience working in highly regulated environments with strong model risk, governance, or control expectations
- Experience designing scalable system architectures for AI products and platforms across multiple stakeholder groups
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