QA Software Engineer 10350 – L2/L3 Networking | Python Automation | VxLAN EVPN

Extreme Networks; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Extreme Networks seeks a QA/Automation Engineer with 2–5 years of hands-on experience in functional, system test, and automation for data center networking. You will work on IP Fabric, VxLAN EVPN, and network virtualization, with exposure to AI-assisted QA tools and cloud environments.

Strong scripting, testing, and debugging skills are required. The role involves test planning, cross‑functional collaboration, and continual learning in a fast‑paced networking context.

Qualifications

• BS or MS in EE/CS with 2 to 5 years of hands-on experience in functional, system test, and automation.
• Solid technical knowledge of data center networking — IP Fabric, VxLAN EVPN, and network virtualization concepts.
• Working knowledge of Ethernet, optics, and networking hardware.
• Knowledge of routing protocols (OSPF, IS-IS, BGP, Multicast) and network security fundamentals.
• Hands‑on experience developing test automation using Python or Golang.
• Experience with test planning, requirement-to-testcase mapping, defect logging and tracking, and debugging.
• Exposure to AI/ML concepts or AI-assisted developer/testing tools applied to QA workflows.
• Strong verbal and written communication skills and the ability to collaborate cross‑functionally.
• Highly motivated, self-driven, and eager to learn.

Responsibilities

• Develop and execute test plans for data center networking features.
• Create and maintain test scripts in Python or Golang.
• Collaborate with cross‑functional teams to validate product features.
• Utilize AI‑assisted tools for test case generation and data analysis.
• Perform test result analysis and defect reporting.

Skills

Python

Golang

Test automation

CI/CD

Docker

Cloud (AWS/Azure/GCP)

AI/ML in QA

Communication

Self-motivated learning

Education

BS in EE/CS

MS in EE/CS

Tools

Wireshark

Jenkins

Git

Ixia/Spirent

Docker

Job description

Qualifications And Requirements

Experience: 2-5 Years

• BS or MS in EE/CS with 2 to 5 years of hands-on experience in functional, system test, and automation.


• Solid technical knowledge of data center networking — IP Fabric, VxLAN EVPN, and network virtualization concepts.


• Working knowledge of Ethernet, optics, and networking hardware.


• Knowledge of routing protocols (OSPF, IS-IS, BGP, Multicast) and network security fundamentals.


• Hands‑on experience developing test automation using Python or Golang.


• Experience with test planning, requirement-to-testcase mapping, defect logging and tracking, and debugging.


• Exposure to AI/ML concepts or AI-assisted developer/testing tools (e.g., LLM-based assistants, GenAI copilots) applied to QA workflows.


• Strong verbal and written communication skills and the ability to collaborate cross‑functionally.


• Highly motivated, self-driven, and eager to learn.

Skillset Required

Good knowledge and hands‑on experience across most of the following areas:

Networking

• IEEE 802.1 (Bridging, VLAN, STP, MAC security, LLDP).


• L2/L3 features (TCP/IP, VRRP, IGMP, IPv4/IPv6, ICMP/ICMPv6, ARP); basic IS-IS/BGP.


• Network debugging tools (Wireshark, ping, traceroute) and traffic generators (Ixia/Spirent).

Test Automation

• Test scripting in Python or Golang; familiarity with automation frameworks and CI/CD (Jenkins/GitLab).


• Version control (Git) and defect/test management tools (JIRA, qTest).


• Exposure to Docker containerization and cloud environments (AWS, Azure, GCP) is a plus.

AI in the Test Cycle

• Familiarity with using AI assistants to generate/augment test cases and test data.


• Interest in AI-based log analysis, failure triage, and test-coverage gap detection.


• Understanding of prompt basics for applying GenAI tools responsibly within QA workflows.

Methodology

Knowledge of testing methodologies, testing types, and the overall 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.