Extreme Networks
Job Description:
Experience : 8-13+ Years • BS or MS in EE/CS with 8+ years of hands-on experience in functional, system test, and automation, including a track record of technical leadership. • Expert technical knowledge of data center networking — IP Fabric, VxLAN EVPN, and network virtualization frameworks. • Expert knowledge of Ethernet, optics, and networking hardware. • Expert knowledge of network security and routing protocols (OSPF, IS-IS, BGP, Multicast). • Proven experience architecting large-scale system test topologies and automation frameworks using Python or Golang. • Demonstrated leadership in introducing AI/ML or GenAI into QA — building or adopting AI-assisted testing, triage, or analytics capabilities at team or org scale. • Deep experience in performance, scale, and convergence testing and in analyzing and improving system-level performance. • Ability to author and publish solution validation documents, reference architectures, and test reports. • Excellent communication skills and the ability to influence at all levels of the organization. • Highly motivated, self-driven, and able to lead cross-functionally toward challenging goals. Skillset Required Deep expertise and demonstrated leadership across most of the following areas: Networking • IEEE 802.1 (Bridging, VLAN, STP, MAC security, LLDP, AVB) and advanced L2/L3 (TCP/IP, VRRP, IGMP, IPv4/IPv6, ICMP/ICMPv6, ARP, IS-IS, BGP, Multicast). • Data center fabric design, network virtualization (VMware NSX, OpenStack), and network security architecture. • Traffic generators (Ixia/Spirent) and advanced debugging (Wireshark, packet analysis). Test Automation • Architecting automation frameworks in Python/Golang and defining CI/CD strategy (Jenkins/GitLab). • Automation for end-to-end solution validation, integrated for seamless, continuous testing. • Docker containerization, clustering, and cloud environments (AWS, Azure, GCP). AI in the Test Cycle • Strategy and rollout of AI-assisted test-case generation, intelligent test selection and prioritization, and self-healing automation. • AI/ML-based log analysis, automated failure triage, anomaly detection, and predictive coverage/quality analytics. • Responsible-AI practices and governance for applying GenAI tooling within QA workflows. Leadership & Methodology • Test strategy ownership, mentoring, and setting engineering standards. • Deep knowledge of testing methodologies, testing types, and the full product life cycle.