QA Software Engineer 10350 – L2/L3 Networking | Python Automation | VxLAN EVPN
Extremenetworks
| Company | Extremenetworks |
| Category | Uncategorised |
| Location | Bangalore |
| Remote | Hybrid |
| Employment | Full-time |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 28 Jul 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (lever) |
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
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