Project Description :

Product verification and testing for an L1 - L3 Traffic Generator Solution for a Global Networking Testing and Validation equipment provider.

Our team : A team of networking enthusiasts willing to contribute to customer success.

Technologies used : Networking protocols, traffic generation tools, Windows/Linux servers, network virtualization, network management tools, microservices.

Opportunities : Networking and testing certifications, collaboration with an international team of professionals, working with the latest networking technologies and concepts, access to training.

Skills required :

- Overall QA background of 6+ years, with 4+ years in testing networking domain-related applications.

- QA Automation experience in an Agile/SCRUM team, focused on API and/or CLI testing (4+ years); Python strongly preferred (other comparable languages considered).

- Automated Testing for API or CLI 4+ years, including experience in test harness or framework development.

- Hands-on experience automating Spirent TestCenter (STC) using its Python API - specifically StcPython native API, the REST-based stcrestclient library (py-stcrestclient), or HLTAPI (High Level Test API); ability to script traffic port configuration, device emulation, traffic stream setup, result subscription and statistics retrieval.

- Familiarity with STC Lab Server architecture : connecting to chassis, managing test sessions, and executing automated sequences without GUI dependency.

- Experience writing and maintaining long-running automation suites (soak tests of days/weeks duration) with robust result collection, error handling, and reporting.

- Working knowledge of L2/L3 protocols exercised through STC : Ethernet, VLAN/QinQ, BGP (v4/v6), OSPF (v2/v3), IS-IS, MPLS/LDP, BFD, and data-center protocols such as VXLAN and EVPN; ability to configure emulated/simulated router topologies for scale and convergence testing.

- Familiarity with RFC 2544 / RFC 2889 benchmarking methodology for throughput, latency, frame loss and back-to-back testing.

- Practical experience with AI tooling in day-to-day activities to improve productivity and delivery quality (code generation, code review, automation, agentic AI, MCP usage, etc.).

- Experience in performance, scale, and convergence testing; ability to analyze and improve performance and convergence at system level.

- Ability to develop and publish solution validation documents and test reports.

- Strong verbal and written communication skills; able to communicate effectively across QA and Development teams.

- Highly motivated, self-driven, and able to collaborate cross-functionally to meet challenging goals.

Nice To Have :

- Knowledge Linux OS variants (Ubuntu, Debian).

- CI/CD (Jenkins, GitLab, or similar) and issue tracking tools (JIRA, Confluence).

- Knowledge of STC Tcl API or legacy Tcl/HLTAPI scripting (useful for migrating existing test suites to Python).

- Familiarity with additional traffic generation or network testing tools (e.g., IXIA/Keysight, Wireshark, Iperf).

Responsibilities :

- Design, implement and maintain long-running automated test scenarios (soak tests spanning days/weeks) using Spirent TestCenter APIs, covering performance, scalability, convergence, and protocol stability under sustained traffic load.

- Derive automation requirements from manual functional test specifications and hardware validation reports.

- Collaborate with development team to translate software requirements into high-quality test automation suites.

- Develop automation for the end-to-end solution validation and integrate the automation for seamless validation.

- Implement, execute and debug Python automated test scripts on Linux-based platforms.

- Analyze and manage automated regression test execution failures.

- Debug and troubleshoot complex issues and work closely with the engineering team on the failures and validate the fixes.

- Integrate test suites into CI/CD pipelines.

- Collaborate closely with hardware and software development teams to triage test failures and drive quality improvements.

- Reproduce and root-cause hardware/firmware defects through systematic test isolation.

- Make use of AI tooling (e.g. Claude Code, agentic AI) given customer-specific heavy usage patterns.

- Review customer deployments and customer-found issues and address any gaps in the test coverage.