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 AI and Machine Learning Data Platforms 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 breakdown technical problems
Develops secure and high-quality production code, and reviews and debugs code written by others
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
Will provide technical leadership on data projects across engineers, product managers and stakeholders
Will provide mentorship/guidance to a couple of junior engineers
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.
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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.
Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and advanced applied experience
Hands-on practical experience delivering system design, application development, testing, and operational stability
Demonstrable strength in a cloud based engineering environment
Advanced Python - additional programming languages a plus
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
Proficient in all aspects of the Software Development Life Cycle
Good understanding of databases / data structures
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
In-depth knowledge of the financial services industry and their IT systems
Practical cloud native experience
