Lead Software Engineer - Proxy/sse Network Security

JPMorgan Chase JPMorgan Chase · Banking · Plano, TX +1 · Corporate Sector

Lead Software Engineer role focused on network security, specifically US perimeter, proxy, and SSE engineering. The role involves driving team adoption of AI-assisted engineering practices, defining standards for perimeter controls, and providing engineering leadership across edge and connectivity adjacencies. It requires experience in delivering regional execution ownership, supervising engineers, and strong knowledge of network security architecture.

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. Develops secure high-quality production code, and reviews and debugs code written by others
  3. 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.
  4. 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.
  5. Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems

Skills

Required

  • Formal training or certification on software engineering concepts and 5+ years applied experience
  • Hands-on practical experience delivering system design, application development, testing, and operational stability
  • Advanced in one or more programming language(s)
  • 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
  • Demonstrated experience delivering regional execution ownership in the US (or North America) for infrastructure and/or security platforms, including prioritization, cross-team coordination, and sustained accountability for operational and delivery outcomes.
  • Experience supervising engineers and delivering cross-team remediation, modernization, and platform programs with clear scope, dependency management, delivery milestones, and measurable outcomes.
  • Strong knowledge of network and perimeter security architecture and controls, including segmentation, routing and policy considerations, encryption and access control patterns, and defense-in-depth design principles.
  • Experience designing, delivering, or operating proxy and/or SSE capabilities at enterprise scale, including the ability to translate security requirements into deployable patterns and operational guardrails.
  • Strong experience translating security requirements into deployable edge and perimeter-adjacent connectivity patterns, including Cisco and Arista environments, and the ability to align engineering decisions to operational and control requirements.
  • Working knowledge of interconnect and colocation connectivity models (including Equinix Fabric) and cloud adjacency pattern

What the JD emphasized

  • 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