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 Corporate Sector - Infrastructure Platforms - Data and Speciality Services 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. 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
- Establish AI-native engineering practices with robust validation standards to ensure speed never compromises correctness
- Work AI-native across the software development life cycle, using AI-assisted development, code review, test generation, and incident analysis while maintaining validaton standards
- 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.
- Own the design, delivery, and operation of enterprise-scale infrastrucutre services from architecture through production
- Write and review production code, maintaining a hands-on apporach and setting the bar for engineering quality
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
- Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
- Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies
- Adds to team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
- Formal training or certificaiton on software engineering concepts and 5+ years applied experience
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- 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
- Advanced hands on expereince engineering with production code experience in one or more programming industry-standard language(s) and framework(s) (e.g., Python, GO, Java, C++, Rust, React, Full Stack, etc.)
- Deep experience building and operating systems in at least one infrastructure domain (cloud, networking, compute, storage, security, or data infrastructure)
- Experience running production systems at scale, including on-call ownership, indcient response, and desinging for reliability and operability
- Understands how to lead and mentor engineers, with setting technical direction
- 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
Preferred qualifications, capabilities, and skills
- Experience across multiple infrastructure domains or programming languages
- Track record of reducing operational toil and cost through automation and better engineering
- Experience adopting AI-native engineering practices at team or organizational scale
- Prior experience in regulated or large-scale enterprise environments
- Experience with greenfield builds and establishing engineering culture
- Ability to influence engineering patterns beyond the immediate team