Lead Software Engineer

JPMorganChase•Plano, TX

About The Position

As a Lead Software Engineer at JPMorganChase within the Corporate Sector – Infrastructure Platforms, you will contribute to an agile team building and enhancing secure, stable, and scalable technology products. You will lead technical work across infrastructure engineering technologies and collaborate with other platforms to architect and implement changes that address issues and modernize technology and processes. The role includes designing, developing, testing, and troubleshooting software; maintaining production quality and operational stability; and promoting effective, responsible use of enterprise-authorized AI tools in engineering and infrastructure work. You will establish validation practices for AI-assisted outputs and software changes, with attention to correctness, performance, security, resiliency, data sensitivity, and auditability. You will also evaluate vendor and internal technology proposals and consider upstream and downstream system impacts.

Requirements

Formal training or certification in software engineering concepts and at least 5 years of applied experience.
Hands-on experience delivering system design, application development, testing, and operational stability.
Advanced proficiency in one or more programming languages.
Experience leading effective use of approved AI-assisted software development tools for coding, code review, test acceleration, or troubleshooting, and setting expectations for validating outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and resiliency and security expectations.
Experience coaching engineers on safe, compliant adoption of AI-assisted tools in delivery practices.
Advanced understanding of agile methodologies, including CI/CD, application resiliency, and security.
In-depth knowledge of the financial services industry and its IT systems.
Deep knowledge of one or more infrastructure engineering areas, such as hardware, networking, databases, storage engineering, deployment practices, integration, automation, scaling, resilience, or performance assessments.
Deep knowledge of Apigee infrastructure technology and Python scripting.
Deep knowledge of cloud infrastructure and multiple cloud technologies, with the ability to operate in and migrate across public and private clouds.
Ability to review and validate AI-assisted recommendations before implementation, escalating when uncertain and ensuring outcomes meet resiliency, security, and auditability expectations.

Nice To Haves

Kong experience.
AWS certification(s).
AI certification(s).

Responsibilities

Execute software design, development, and technical troubleshooting, including creative solutions to problems of moderate complexity.
Develop secure, high-quality production code and review and debug code written by others.
Lead adoption of enterprise-authorized AI-assisted engineering practices, establishing standards for validating outputs through secure coding, peer review, and automated testing.
Use software development lifecycle tools and authorized AI-assisted development and automation capabilities to improve the value of automation.
Identify opportunities to eliminate or automate recurring remediation to improve application and system stability.
Lead evaluation sessions with external vendors, startups, and internal teams to assess architectural designs, technical credentials, and suitability for existing systems and information architecture.
Drive a workstream or project involving infrastructure engineering technologies and collaborate with other platforms to implement changes, resolve issues, and modernize processes and technology.
Consider upstream and downstream data, systems, and technical implications, and advise on mitigation actions.
Apply reuse-first, AI-assisted delivery and automation practices to identify recurring issues and validate remediation options, ensuring changes are traceable, auditable, resilient, and secure.
Use enterprise-authorized AI capabilities to accelerate infrastructure analysis and design documentation while validating outputs and handling operational data according to sensitivity and security requirements.
Review and validate AI-assisted recommendations before implementation, escalating when uncertain.