Lead Infrastructure Engineer- Network
Assume a vital position as a key member of a high-performing team that delivers infrastructure and performance excellence. Your role will be...
Assume a vital position as a key member of a high-performing team that delivers infrastructure and performance excellence. Your role will be instrumental in shaping the future at one of the world's largest and most influential companies.
As a Lead Infrastructure Engineer at JPMorgan Chase within the Employee Platforms 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
- Deliver infrastructure projects of moderate scope by applying strong troubleshooting and problem-solving skills
- 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.
- Lead workstreams or small projects across one or more infrastructure engineering technologies
- Partner with other platform teams to design and implement solutions that resolve issues and modernize the environment
- Design, build, and troubleshoot solutions for moderately complex technical problems
- Evaluate upstream/downstream impact of changes and recommend practical mitigations
- Create clear documentation, including implementation details, processes, and operational runbooks
- Communicate technical strategy, decisions, and outcomes clearly to managers and stakeholders
- 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.
- 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 in infrastructure disciplines, plus 5+ years of relevant experience
- Experience with network architecture, design, and engineering in large enterprise environments
- Hands-on expertise with data center networking platforms such as Cisco NX-OS, Arista EOS, and Cisco ACI
- Strong routing experience with BGP, OSPF, and IS-IS
- Practical experience with Cisco and Arista hardware platforms (e.g., Nexus Series, Catalyst)
- Experience with SDN and overlay networking, including Cisco ACI and MP-BGP EVPN/VXLAN
- Solid knowledge of switching fundamentals (VLANs, STP, vPC/MLAG, stacking) and network segmentation
- Proficiency with automation using Ansible playbooks, data modeling, and Jinja2 templating
- Experience with network automation/management tooling such as Arista CVP/AVD, Cisco NFM, and Cisco Network as Code
- 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, capabilities, and skills
- Python (or another programming language used for network automation)
- Practical understanding of DevOps concepts applied to infrastructure and automation
- Exposure to public cloud infrastructure and services (e.g., AWS)
- Experience with IP Multicast routing in enterprise or data center environments
- Familiarity with high-performance networking technologies (RoCE, InfiniBand, GPUDirect/Data Direct I/O) for HPC workloads
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