Lead Software Engineer

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As a Lead Software Engineer at JPMorganChase within the Corporate Sector – Infrastructure Platforms, 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
- 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.
- 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
- Applies technical expertise and problem-solving methodologies to projects of moderate scope and executes creative solutions for design, development, and technical troubleshooting for problems of moderate complexity
- Uses enterprise-authorized AI capabilities within the work environment to accelerate infrastructure analysis and design documentation, validating outputs and handling operational data according to sensitivity and security requirements.
- Drives a workstream or project consisting of one or more infrastructure engineering technologies and works with other platforms to architect and implement changes required to resolve issues and modernize the organization and technology processes
- Strongly considers upstream and downstream data and systems or technical implications and advises on mitigation actions
- Applies reuse-first, AI-assisted practices within delivery and automation routines to identify recurring issues and validate remediation options, ensuring changes are traceable/auditable and aligned to resiliency and security expectations.

Required qualifications, capabilities, and skills

- Formal training or certification on software engineering concepts and 5+ years applied experience
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Advanced in one or more programming language(s)
- 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 understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- In-depth knowledge of the financial services industry and their IT systems
- Deep knowledge of one or more areas of infrastructure engineering such as: hardware, networking terminology, databases, storage engineering, deployment practices, integration, automation, scaling, resilience or performance assessments
- Ability to review and validate AI-assisted recommendations before implementation, escalating when uncertain and ensuring outcomes align to resiliency, security, and auditability expectations.
- Deep knowledge of Apigee infrastructure technology and scripting languages (Python)
- Deep knowledge of cloud infrastructure and multiple cloud technologies with the ability to operate in and migrate across public and private clouds

Preferred qualifications, capabilities, and skills

- Kong is desirable
- AWS Certification(s)
- AI Certification (s)