Lead Software Engineer - Full-Stack

Responsibilities

• Design, develop, and deploy scalable full-stack platform tools for infrastructure teams.
• Architect and optimize software systems that support core internal services and tools.
• Enhance the reliability and performance of critical internal systems through engineering best practices.
• Collaborate with engineering and operations groups to translate user needs into high-quality applications.
• Automate workflows and monitoring processes to streamline operations and reduce manual effort.
• Review code, mentor junior engineers, and contribute to team knowledge sharing.
• Leverage enterprise-authorized AI capabilities to accelerate data architecture analysis and decisioning.

Requirements

• 3+ years of applied software engineering experience with formal training or certification.
• Professional experience building full-stack applications for infrastructure or platform teams.
• Proficiency in modern programming languages such as JavaScript/TypeScript, Python, Java, or Go.
• Experience developing and deploying microservices and web applications.
• Strong grasp of database systems (SQL and NoSQL), RESTful APIs, and cloud infrastructure concepts.
• Familiarity with CI/CD pipelines, DevOps practices, and automated testing methodologies.
• Demonstrated ability to use AI capabilities within a work environment to support data architecture workflows.

Preferred Qualifications

• Experience with container orchestration tools like Kubernetes or Docker in production environments.
• Knowledge of observability, monitoring, and logging solutions for large-scale systems.
• Background in security compliance or distributed infrastructure design.
• Active participation in developer communities or contributions to open source projects.

About the Company

JPMorgan Chase is a leading global financial services firm. You will be part of the Infrastructure Platforms team, developing the tools and platforms that drive the core of our engineering ecosystem, designing impactful solutions focused on scalability and operational efficiency.