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Staff QA Engineer 10348 – Data Center Networking | Python Automation|Layer2/Layer3
Bengaluru · Hybrid · Posted- Location
- Bengaluru
- Level
- Lead
- Experience
- 8–13 years
- Pay
- Not listed
- Workplace
- Hybrid
- Posted
- 3 months ago
- Status
- Open · checked 11h ago
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Skills mentioned in the posting
- Python
- Golang
- CI/CD
- Jenkins
- GitLab
- Docker
- AWS
- Azure
- GCP
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.