Sr Lead Software Engineer - AI Engineer

JPMorgan Chase & Co.•Columbus, OH

2d•$175,750 - $260,000

About The Position

As a Senior Lead Software Engineer at JPMorganChase within Consumer & Community Banking on the Core Engineering Services team, you will shape and deliver technology platforms that enable teams to build and ship secure, stable, and scalable products. You will drive measurable business outcomes by setting technical direction, leading cross-functional execution, and applying deep engineering expertise to complex challenges across multiple systems and applications.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience
Proven ability to lead end-to-end design and delivery of complex software systems, including architecture, development, testing, and operational readiness
Strong system design skills, including experience with distributed systems, microservices, and application programming interfaces
Proven track record using AI coding assistants (GitHub Copilot, Cursor, Claude, etc.) to accelerate development cycles, with the 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.
Demonstrated ability to drive an AI-augmented engineering culture — setting standards, running experiments, and building team confidence as tooling and best practices evolve in real time.
Experience building and operating large-scale, high-performance digital applications using modern cloud technologies (for example: Amazon Web Services, Microsoft Azure, or Google Cloud Platform)
Demonstrated ability to lead and mentor engineers and senior technical contributors, and to raise engineering standards through coaching and influence
Experience implementing industry-standard security practices and technology controls throughout the software delivery lifecycle
Demonstrated experience leading effective use of AI-assisted software development tools (for example: for coding, code review, test acceleration, and troubleshooting), with the ability to set 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 resiliency and security expectations, with experience coaching senior engineers and leads on compliant usage patterns and controls

Nice To Haves

Experience leading engineering culture transformation initiatives, including adoption of AI tooling, new development workflows, or modern technical practices across distributed teams
Advanced experience with one or more cloud providers (Amazon Web Services, Microsoft Azure, or Google Cloud Platform)
Experience building developer platforms or “paved road” capabilities (for example: reusable frameworks, golden paths, and self-service tooling) that increase engineering throughput and quality
Experience designing and delivering solutions that use large language model (LLM) capabilities in production with strong governance, observability, and human-in-the-loop validation

Responsibilities

Define and drive the technical strategy for modern engineering platforms, aligning architecture, delivery plans, and success metrics to business outcomes
Lead multiple technology implementations across teams and functions, ensuring alignment to enterprise standards for quality, reliability, and security
Partner with senior leaders and key stakeholders to clarify priorities, manage trade-offs, and build consensus across competing objectives
Influence peer engineering leaders across business, product, and technology to improve delivery effectiveness and raise engineering standards
Guide teams through complex, cross-product programs by establishing clear operating rhythms, accountability, and transparent progress reporting
Build and strengthen an inclusive team culture focused on collaboration, continuous learning, and shared ownership of outcomes
Drive adoption and governance of AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes (for example: AI-assisted code review and refactoring, test acceleration, release readiness, and incident/root-cause analysis), while setting measurable validation standards and promoting reuse of proven patterns
Apply knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to increase the value realized by automation at scale