Network Engineer, NetOps Infrastructure
$108,400–$108,400 year
On-siteDenver, Colorado, United States
Job Summary
Troubleshoot production network issues end-to-end, managing routing tables, firewall policies, and Linux network stacks to maintain 99.999% availability. Handle operational tasks including firewall access changes, patching, config reviews, and capacity checks while delivering infrastructure projects like hardware refreshes and platform migrations. Work directly with platform and application teams to support Lab, Stage, and Regression environments alongside production systems. This role spans the US, EU, and India, requiring five-day coverage following the sun with occasional weekend work during maintenance windows. You will operate within an AI-forward engineering org where AI assistants are integrated into daily workflows for config review and log analysis.
Required Qualifications
- 3+ years running production networks — ISP, enterprise, or data center
- Real depth in TCP/IP
- Routing and switching in practice: OSPF, iBGP/eBGP, VRFs, PBR, STP/RSTP, VRRP, link aggregation
- Tunneling: GRE, IPIP, IPSec
- Firewalls, both vendor platforms and iptables / firewall
- Strong Linux, including the network stack — interfaces, routing tables, netfilter, socket state, tcpdump
- Enough server-side context (bare metal, hypervisors, containers) to follow a problem past the switch port
- Hands-on Juniper experience across routing, switching, and security platforms
- Occasional weekend work during planned maintenance windows
- You work your own business hours, five days a week
- No night shifts
Desired Qualifications
- Nice to have EVPN/VXLAN, MC-LAG, ECMP, MPLS
- High availability and clustering
- Load balancing (A10, F5, IPVS)
- Scripting for your own workflows — Bash, Python, NETCONF, PyEZ, Ansible
- Cloud networking
- NetBox, Zabbix, VictoriaMetrics, Grafana
- You've used AI assistants on real engineering work, not just tried them
- You can scope a prompt well and iterate when the first answer is wrong
- You verify before you trust. Confidently wrong output is the failure mode that matters in production.
- Validate against docs and lab testing
- You follow data-handling policy on what goes into external tools
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