As a Lead Infrastructure Engineer at JPMorgan Chase within the Infrastructure Platforms, Network Services team, you apply deep knowledge of software, applications, and technical processes within the infrastructure engineering discipline. Continue to evolve your technical and cross-functional knowledge outside of your aligned domain of expertise.

Job Responsibilities 
•    Drives a workstream or project consisting of one or more infrastructure engineering technologies and works with other platforms to drive changes required to resolve issues and modernize the organization and technology processes
•    Strongly considers upstream and downstream data and systems or technical implications and advises on mitigation actions
•    Applies technical expertise and problem-solving methodologies to projects of moderate scope and executes creative solutions for design, development, and technical troubleshooting for problems of moderate complexity
•    Partner with risk, audit, and compliance. Translate regulatory requirements into sound engineering decisions. Serve as subject matter expert to internal auditors and, where appropriate, to regulators.
•    Develop other engineers. Mentor across the team on technical depth and engineering judgment. Lead architecture reviews, ADRs, and design discussions. Help identify and grow the next generation of senior engineers — even though you will not line-manage them.
•    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.
•    Applies reuse-first, AI-assisted practices within delivery and automation routines to identify recurring issues and validate remediation options, ensuring changes are traceable/auditable and aligned to resiliency and security expectations.

Required qualifications, capabilities, and skills
•    Formal training or certification on infrastructure engineering concepts and 5+ years applied experience 
•    Deep knowledge of one specific infrastructure technology and scripting languages
•    Deep knowledge of cloud infrastructure and multiple cloud technologies with the ability to operate in and migrate across public and private clouds
•    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
•    Curiosity and systems thinking. You ask the right questions, hold the broader picture in view, and can explain how interconnected systems fit together.
•    Network fabric and connectivity — design and operational depth in routing, switching, wireless, and WAN across any of our preferred technologies listed below.
•    Security, edge and segmentation — experience securing the perimeter and enforcing Zero Trust segmentation using any of our preferred technologies listed below.
•    Engineering toolchain proficiency—ability to automate, version-control, and observe network/infrastructure services using modern engineering practices
•    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.

Preferred qualifications
• Network fabric and connectivity: Cisco, Juniper, Arista, Juniper Mist, Cisco/Viptela, Fortinet; BGP, OSPF, MPLS, EVPN/VXLAN, SD-WAN; enterprise Wi Fi (Wi Fi 6E / Wi Fi 7), 5G / private cellular.
• Security, edge and segmentation: Palo Alto, Zscaler; IPsec, TLS, ZTNA, micro-segmentation.
• Engineering toolchain: Python, Ansible, Terraform, Git, GitHub Actions / Jenkins; ThousandEyes, Splunk, Grafana, Wireshark.
• AI as an engineering tool: comfort using approved AI assistants to accelerate analysis, scripting, documentation, and troubleshooting; we are looking for engineers who use AI tooling as a force multiplier — not engineers who build AI/ML systems.