Lead Software Engineer – AI-Native Component Engineering
JPMorgan Chase · Columbus, United States · 5d ago
Staff / Lead / PrincipalOn-siteSoftware Engineering
The Chief Technology Office oversees enabling components inclusive of the top quality engineering and architecture tools and practices, key program management and processes as well as the technology workforce strategy required to make us a leading technology company for our customers, clients and colleagues around the world.
High Risk Roles (HRR) are sensitive roles within the technology organization that require high assurance of the integrity of staff by virtue of 1) sensitive cybersecurity and technology functions they perform within systems or 2) information they receive regarding sensitive cybersecurity or technology matters. Users in these roles are subject to enhanced pre-hire screening which includes both criminal and credit background checks (as allowed by law). The enhanced screening will need to be successfully completed prior to commencing employment or assignment.
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorganChase within the Chief Technology Office, 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. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems.
- Develops secure, high-quality production code and reviews and debugs code written by others.
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems.
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
- Builds components to be drop-in and AI-native with automated upgrades so adopters across the enterprise stay current with minimal effort.
- Leads communities of practice across Software Engineering to drive awareness and adoption of leading-edge technologies and shared frameworks.
- Adds to 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.
- Demonstrated experience leading effective use of approved AI-assisted software development tools (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 engineers on safe, compliant adoption within delivery practices.
- Hands-on practical experience delivering system design, application development, testing, and operational stability.
- Advanced skills in Java, JavaScript, or Python.
- Proficiency in automation and continuous delivery methods.
- Proficient in all aspects of the Software Development Life Cycle.
- Advanced understanding of agile methodologies such as CI/CD, application resiliency, and security.
- Demonstrated proficiency in a technical discipline such as cloud, artificial intelligence, or machine learning.
- Practical cloud-native experience.
Preferred qualifications, capabilities, and skills
- Experience building SDKs, frameworks, or developer platforms that other engineering teams consume.
- Experience with AI-native tooling and AI-assisted development workflows.
- Demonstrated code shipped to production that leverages AI or builds AI capabilities.