Lead Software Engineer - Linux Infrastructure Platform

JPMorgan Chase JPMorgan Chase · Banking · San Francisco, CA +1 · Corporate Sector

Lead Software Engineer for Linux Infrastructure Platform at JPMorgan Chase, focusing on automating OS image creation, package management, and provisioning for on-premise and cloud environments. The role involves using AI tools for testing, development, and improving code quality, delivery speed, and operational outcomes, while ensuring responsible AI use and adherence to security standards.

What you'd actually do

  1. 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.
  2. 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.
  3. Address complex technical issues and develop integration elements and APIs.
  4. Inspire and lead a team with a passion for leading-edge technologies.
  5. Join the Linux Engineering team to deliver a Standard Operating Environment to the firm.

Skills

Required

  • Linux Infrastructure development
  • OS-level configuration
  • system hardening
  • kernel tuning
  • package management
  • building scalable, automated infrastructure pipelines
  • cloud
  • hybrid
  • virtualized infrastructure
  • modern software languages
  • Ansible Core or Ansible Automation Platform
  • developing playbooks, workflows, and reusable roles and modules
  • container technologies
  • Kubernetes-based orchestration platforms
  • Python programming
  • web application frameworks
  • continuous integration and delivery pipelines
  • source control
  • build automation tools
  • Windows Server operating systems
  • REST API based microservices
  • Ansible design, architecture and deployment processes
  • responsible AI use
  • data sensitivity considerations
  • secure handling of inputs/outputs
  • resiliency and security expectations

Nice to have

  • content delivery and repository management systems
  • Red Hat Satellite
  • Pulp
  • JFrog Artifactory
  • software distribution
  • patch lifecycle
  • content views across enterprise environments
  • development of AI Agents and Skills
  • building agentic workflows
  • integrating large language models into infrastructure tooling
  • developing AI-driven automation skills
  • Citrix Virtualization Infrastructure
  • initiate and implement ideas to solve business problems
  • learning new technologies
  • driving innovative solutions

What the JD emphasized

  • responsible AI use in engineering workflows
  • demonstrated experience leading effective use of approved AI-assisted software development tools