Lead Software Engineer - Linux Infrastructure Platform

Become a member of a team where you can contribute significantly to shaping the future of a world-renowned and influential company. Among top performers, you can make a direct and meaningful impact.

As a Lead Software Engineer
- Linux at JPMorgan Chase within the Corporate Sector Compute Infrastructure Platform (CIP) organization, you exhibit both depth and breadth of knowledge regarding software, applications, and technical processes across multiple technical disciplines. You also have a specialization in a specific domain within infrastructure engineering to drive programs or initiatives consisting of multiple technologies and applications.

• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems.
• Using AI to test and certify new Red Hat operating systems, create multiple image formats, and develop end-to-end automation capabilities using Ansible, Terraform, Jenkins, Bash, and Python, while exploring opportunities to learn and code with Python, Go, Rust and AI agents like Claude and Co-Pilot.
• Address complex technical issues and develop integration elements and APIs.
• Inspire and lead a team with a passion for leading-edge technologies.
• Leverage the global network to tackle significant challenges and drive innovation.
• Join the Linux Engineering team to deliver a Standard Operating Environment to the firm.
• Create standard OS Images for Virtual and Physical servers, Desktop, GPU, and Containers for both on-premise and external Clouds.
• Provide services to host and manage software (primarily RPMs) across all clouds.
• Automate the provisioning of Linux Desktops for developers and streamline the testing and deployment of RPM packages to production.
• 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.

• Formal training or certification on software engineering concepts and 5+ years applied experience.
• Demonstrates strong skills in Linux Infrastructure development, including OS-level configuration, system hardening, kernel tuning, package management, and building scalable, automated infrastructure pipelines across on-premise and cloud environments.
• Experience in cloud, hybrid, virtualized infrastructure, and modern software languages.
• Demonstrate hands-on experience designing and implementing infrastructure automation solutions using Ansible Core or Ansible Automation Platform, including developing playbooks, workflows, and reusable roles and modules.
• Apply practical experience working with container technologies and Kubernetes-based orchestration platforms, with the ability to deploy and manage workloads in enterprise-grade environments.
• Leverage proficiency in Python programming to build and maintain automation solutions, with working knowledge of web application frameworks and continuous integration and delivery pipelines using source control and build automation tools.
• Utilize experience with Windows Server operating systems to support cross-platform infrastructure solutions and ensure compatibility across diverse technology environments.
• Experience creating REST API based microservices.
• Candidate should be able to give feedback on Ansible design, architecture and deployment processes.
• 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

• Prior knowledge of content delivery and repository management systems such as Red Hat Satellite, Pulp, or JFrog Artifactory is a significant plus. Experience managing software distribution, patch lifecycle, and content views across enterprise environments is highly valued.
• Experience with the development of AI Agents and Skills is a plus. Familiarity with building agentic workflows, integrating large language models into infrastructure tooling, or…