Lead Infrastructure Engineer - Technical Solutions Engineering

JPMorgan Chase JPMorgan Chase · Banking · Palo Alto, CA +1 · Corporate Sector

Lead Infrastructure Engineer for Cloud Foundational Platforms at JPMorgan Chase. This role involves applying deep knowledge of software, applications, and technical processes within infrastructure engineering. The engineer will drive projects, architect and implement changes, troubleshoot complex problems, and manage stakeholder relationships. A key aspect is the use of enterprise-authorized AI capabilities to accelerate infrastructure analysis, design documentation, and automation routines, with a strong emphasis on validation, data sensitivity, and security.

What you'd actually do

  1. Applies technical expertise and problem-solving methodologies to projects of moderate scope
  2. Drives a workstream or project consisting of one or more infrastructure engineering technologies
  3. Works with other platforms to architect and implement changes required to resolve issues and modernize the organization and its technology processes
  4. Executes creative solutions for the design, development, and technical troubleshooting for problems of moderate complexity
  5. Uses enterprise-authorized AI capabilities within the work environment to accelerate infrastructure analysis and design documentation, validating outputs and handling operational data according to sensitivity and security requirements.

Skills

Required

  • Formal training or certification on Infrastructure engineering concepts and 5+ years applied experience
  • Deep knowledge of one or more areas of infrastructure engineering such as hardware, networking terminology, databases, storage engineering, deployment practices, integration, automation, scaling, resilience, or performance assessments
  • Deep knowledge of one specific infrastructure technology and scripting languages (e.g., Scripting, Python, etc.) and AWS certification(s)
  • Drives to continue to develop technical and cross-functional knowledge outside of the product
  • Deep knowledge of cloud infrastructure and multiple cloud technologies with the ability to operate in and migrate across public and private clouds.
  • Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support infrastructure engineering workflows with strong validation habits and awareness of data sensitivity.
  • Ability to review and validate AI-assisted recommendations before implementation, escalating when uncertain and ensuring outcomes align to resiliency, security, and auditability expectations.
  • A drive to continue to develop technical and cross-functional knowledge
  • Familiarity with working in a large distributed system across a range of technologies including compute, databases, messaging, observability, and telemetry
  • Knowledge of incident, change, and problem management processes and the controls that govern them
  • Understanding of data-driven decision making

Nice to have

  • Kubernetes, Azure, GCP, or Terraform certifications
  • Comfortable exploring AI tools to speed up troubleshooting and documentation, with proper verification and data protection.

What the JD emphasized

  • Deep knowledge of one or more areas of infrastructure engineering such as hardware, networking terminology, databases, storage engineering, deployment practices, integration, automation, scaling, resilience, or performance assessments
  • Deep knowledge of one specific infrastructure technology and scripting languages (e.g., Scripting, Python, etc.) and AWS certification(s)
  • Deep knowledge of cloud infrastructure and multiple cloud technologies with the ability to operate in and migrate across public and private clouds.
  • Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support infrastructure engineering workflows with strong validation habits and awareness of data sensitivity.
  • Ability to review and validate AI-assisted recommendations before implementation, escalating when uncertain and ensuring outcomes align to resiliency, security, and auditability expectations.