AI Agentic Sr Lead Software Engineer

Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We’re proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions – all while ranking first in customer satisfaction.

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As a Senior Lead Software Engineer at JPMorganChase within the Consumer & Community Banking Small Business Lending Team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

Job responsibilities

• Drive the technical strategy and delivery of a Lending platform, embedding AI-native capabilities (agentic workflows, LLM-powered features) into core product experiences.
• Leads multiple technology implementations across departments to achieve firmwide technology objectives.
• Directly manages multiple areas with strategic transactional focus.
• Acts as the primary interface with senior leaders, stakeholders, and executives, driving consensus across competing objectives.
• Manage multiple stakeholders, complex projects, and large cross-product collaborations.
• Influences peer leaders and senior stakeholders across the business, product, and technology teams.
• Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishingmeasurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.   
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale
• Adds to the team culture of diversity, opportunity, inclusion, and respect

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Proven track record using AI coding assistants (GitHub Copilot, Cursor, Claude, etc.) to accelerate development cycles, and ability to coach teams to adopt these tools as standard practice.
• Experience designing and deploying agentic systems (e.g., LLM-powered agents, multi-step reasoning workflows, tool-using AI) in production environments.
• Drives adoption of an AI-augmented engineering culture—setting standards, running experiments, and building team confidence as tooling and best practices evolve in real time.
• Deep expertise in system design, application development, testing, and operational stability for commercially used platforms web and/or mobile.
• Deep expertise in building and operating large scale high performance digital applications (web and/or mobile) with distributed systems and cloud technologies (AWS, GCP, Azure, etc.)
• Deep expertise with enterprise design patterns and industry best practices with experience using modern technologies and design patterns (e.g., micro services, APIs, etc.)
• Experience with building, leading and mentoring technology teams, and next level leaders within the organization. 
• Experience with implementing industry standard cybersecurity & technology controls.

• Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security

• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and

• Experience leading engineering culture transformation initiatives, particularly dirving adoptio of AI tooling, new development workflows or technical practice changes across distributed teams.   Strong experience with Cloud providers (AWS, GCP, Azure)   
• Strong experience with Cloud providers (AWS, GCP, Azure)