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

Job Description:

Qualifications and 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.

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.