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

Sobre esta vaga de QA Software Engineer 10350 – L2/L3 Networking | Python Automation | VxLAN EVPN na Extremenetworks

Extremenetworks · Híbrido · Bangalore

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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