Qualifications and Requirements :

Experience : 8 to 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 :

1. 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).

2. 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).

3. 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.

4. Leadership & Methodology :

- Test strategy ownership, mentoring, and setting engineering standards.

- Deep knowledge of testing methodologies, testing types, and the full product life cycle.