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.