QA Software Engineer - L2/L3 Networking | Python Automation

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.