Lead Software Engineering- Java/Python

Overview

As Lead Software Engineer, you will drive secure, scalable, open-source driven payments technology within the Commercial & Investment Bank. You’ll be a core technical contributor, shaping architecture and delivering trusted products in a fast-evolving, AI-first environment. You will own the public JPMorgan-payments repositories and enable external developers to integrate quickly, while raising code quality through enterprise AI-assisted practices. This role blends hands-on engineering with governance and external collaboration to advance market-leading payments capabilities.

Responsibilities

• Own and maintain the public JPMorgan-payments GitHub presence, ensuring repo hygiene, licensing, CI, dependency management, and release quality
• Develop secure, production-grade code and review others’ code to improve quality
• Evolve flagship public repositories (agent skills, MCP server) enabling external developers to leverage JPMorgan Payments capabilities
• Drive Open Source Governance for publication of external SDKs and libraries, turning pilot products into public packages
• Promote AI-assisted engineering practices (code review, test strategy, incident analysis) and establish validation standards
• Utilize SDLC tooling and AI-assisted automation to improve value and efficiency
• Identify and automate recurring remediation to improve stability of software and systems
• Lead architecture evaluations with external vendors and internal teams for alignment with existing systems
• Represent engineering externally via public repos, technical writing, and conference contributions

Key requirements

• Formal training or certification in software engineering concepts
• Hands-on experience delivering system design, development, testing, and operational stability
• Advanced proficiency in at least one programming language
• Experience maintaining public open-source repositories with issue triage, releases, and security standards
• Experience leading AI-assisted software development tools and setting team expectations for AI output validation
• Strong understanding of responsible AI in engineering workflows, including data sensitivity and security
• Proficiency across the Software Development Life Cycle
• Advanced understanding of agile methodologies (CI/CD, resiliency, security)
• Practical cloud-native experience
• Knowledge of the financial services IT landscape

• leadership and mentorship
• cross-functional collaboration
• external communication and advocacy
• programming languages (advanced)
• open-source repository management
• AI-assisted development tools for coding, review, and testing

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