Staff QA Engineer 10348 – Data Center Networking | Python Automation|Layer2/Layer3

Requirements

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; 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); 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); 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; Test strategy ownership, mentoring, and setting engineering standards; Deep knowledge of testing methodologies, testing types, and the full product life cycle