JPMorgan Chase
Lead Infrastructure Engineer ( SDWAN)- Risk Management
$147K–$190K
San Francisco, CA +1 · Full-time · Posted Oct 25, 2026
- Location
- San Francisco, CA
Plano, TX - Experience
- 5+ years applied experience in infrastructure engineering concepts; the posting also asks for hands-on SD-WAN engineering and complex global network experience without specifying years.
- Education
- Formal training or certification on infrastructure engineering concepts.
About the job
Leads the design, standardization, and delivery of SD-WAN infrastructure, including architecture, implementation, security and resiliency controls, and risk management.
What you'll do
- Lead SD-WAN architecture, evaluation, and standardization, and develop technical design artifacts such as functional architecture and HLD/LLD.
- Define requirements and lead testing and implementation with engineering, product, business, and architecture stakeholders.
- Manage delivery and operational risks, ensuring security, resiliency, and audit-ready controls; use authorized AI capabilities to support engineering workflows.
What they ask for
- Formal training or certification in infrastructure engineering concepts and applied experience.
- Hands-on SD-WAN network engineering across design, implementation, and support; experience supporting complex global network environments.
- Experience producing design artifacts and leading requirements analysis and implementation with stakeholders.
- Advanced knowledge of SD-WAN, core networking/routing, network virtualization, and network automation concepts.
Nice to have
- Industry-recognized SD-WAN certification or experience with major SD-WAN platforms.
- Experience with network automation, production troubleshooting, regulated change-controlled environments, or large-scale network monitoring and analytics.
Pay
USD 147,250.00-$190,000.00 base salary
Tools
SD-WAN, HLD/LLD, Layer 2/Layer 3, API-driven operations, scripting, CAB/ITIL
AI focus
Uses enterprise-authorized AI to support infrastructure analysis, design documentation, delivery, and automation; validates AI outputs and safeguards sensitive data, security, resiliency, and auditability.
Model-written summary · the original posting is authoritative