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

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.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.