Lead Software Engineer - AI & Machine Learning

About the role

You will influence outcomes across a federated delivery model by enabling reuse, aligning stakeholders, and embedding practical frameworks that improve delivery speed, resiliency, and control adherence. As a Lead Software Engineer at JPMorganChase within Corporate Technology and the AI/Machine Learning & Automation Center of Excellence, you will drive scalable adoption of responsible AI and intelligent automation by defining standards, reference architectures, and governance that teams can implement consistently.

JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.

Responsibilities

• Drive enterprise alignment and adoption of AI/machine learning and intelligent automation by partnering with engineering, product, and business teams to embed consistent approaches into delivery roadmaps.
• Influence cross-functional stakeholders to align priorities across federated teams, reduce fragmentation, and drive adherence to Center of Excellence frameworks and standards.
• Define, maintain, and evolve scalable reference architectures, implementation patterns, and standards that accelerate adoption while remaining practical for delivery teams.
• Enable reuse at scale by promoting shared tooling, accelerators, and reusable capabilities that improve time-to-market and consistency.
• Provide advisory support and constructive challenge to delivery teams to strengthen design decisions, implementation quality, and operational readiness of AI-enabled solutions.
• Establish and reinforce Responsible AI governance across distributed teams, including practices for ethical AI use, bias mitigation, data quality, and lifecycle management.
• Promote secure, resilient, and scalable engineering practices aligned to enterprise architecture expectations across cloud, APIs, and integration patterns.
• Lead and contribute to a community of practice to uplift capability, share patterns,