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This listing doesn't post a salary. Most similar roles pay
$150,475–$211,200.
Based on 240 similar postings.
Employer
About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has
1226 open roles
on FindRole.
Listed pay typically runs
$156,750–$215,000
across 5 roles with salary data.
As a Lead Software Engineer - Full Stack / React, you will join the Corporate Sector - Infrastructure Platforms - Data and Speciality Services team. You will serve as a core technical contributor responsible for designing, developing, and operating enterprise-scale infrastructure services from architecture through production. Your daily work involves writing and reviewing production code, troubleshooting complex technical problems, and establishing AI-native engineering practices for code review, test generation, and incident analysis. You will utilize technologies including Python, GO, Java, C++, Rust, and React within an agile framework. Key responsibilities include driving team adoption of automated CI/CD methods, ensuring application resiliency, and leading communities of practice. You will solve technical problems across infrastructure domains like cloud, networking, compute, storage, security, and data infrastructure to ensure stable, scalable software delivery.
Design, deliver, and operate enterprise-scale infrastructure services from architecture through production.
Write and review production code while maintaining a hands-on approach to engineering quality.
Establish and drive the adoption of AI-native engineering practices for code review, test generation, and incident analysis.
Implement robust validation standards to ensure AI-assisted development maintains correctness, security, and performance.
Identify and automate the remediation of recurring issues to improve the operational stability of software systems.
Lead evaluation sessions with external vendors and internal teams to assess architectural designs and technical credentials.
Lead communities of practice to drive the awareness and adoption of leading-edge technologies.
Apply automation and continuous delivery methods across the entire software development life cycle.
What we're looking for
Formal training or certification on software engineering concepts and 5+ years of applied experience.
Hands-on practical experience delivering system design, application development, testing, and operational stability.
Demonstrated experience leading the use of approved AI-assisted software development tools and validating outputs for correctness and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity and secure handling of inputs/outputs.
Advanced hands-on experience engineering production code in one or more industry-standard languages and frameworks like Python, GO, Java, C++, or Rust.
Deep experience building and operating systems in at least one infrastructure domain such as cloud, networking, compute, storage, security, or data.
Experience running production systems at scale, including on-call ownership, incident response, and designing for reliability.
Proficiency in automation, continuous delivery methods, agile methodologies, and all aspects of the Software Development Life Cycle.
Experience across multiple infrastructure domains or programming languages (preferred).
Track record of reducing operational toil and cost through automation and better engineering (preferred).
Experience adopting AI-native engineering practices at team or organizational scale (preferred).
Prior experience in regulated or large-scale enterprise environments (preferred).
Experience with greenfield builds and establishing engineering culture (preferred).
Ability to influence engineering patterns beyond the immediate team (preferred).
Python
ReactJS
Django
FastAPI
CI/CD
Microservices
API
Agile
Cloud Native
AI-assisted Engineering
Software Development Life Cycle
Network Automation
Big Data
Application Resiliency