Network Software Test Engineer II

Network Software Test Engineer II This role has been designed as ‘Hybrid’ with an expectation that you will work on average 2 days per week from an HPE office.

Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next.

We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE.

Aruba, a Hewlett Packard Enterprise Company, is a leading provider of next-generation working solutions for the mobile enterprise:htttp://

• Lead test activities for assigned features by analyzing design documents and technical specifications to define objectives, scope, and deliverables with limited oversight.
• Own development, execution, and refinement of test plans, protocols, and documentation for assigned subsystems; identify gaps and recommend improvements.
• Design, implement, and maintain automated test scripts and harnesses in Python; produce modular, reusable, and well-documented test code used in CI pipelines.
• Apply AI models and LLMs to accelerate test-case generation, prompt-driven scenario creation, log parsing/classification, anomaly detection, and test prioritization.
• Capture and analyze packet traces and system logs (Wireshark, tcpdump); translate findings into clear, reproducible bug reports and root-cause analyses.
• Design and run security-aware test scenarios to validate encryption, authentication, access controls, and threat-informed behaviors; elevate risks appropriately.
• Execute functional, performance, interoperability, regression, and reliability tests in physical and virtual lab environments; drive issue reproduction and verification of fixes.
• Collaborate cross-functionally with QA, development, product, and outsourced partners to coordinate testing, improve coverage, and support release readiness.
• Maintain test environment configurations, version control for test code, and CI/CD integration for automated runs; contribute to production-level test infrastructure.
• Mentor junior team members and provide practical guidance based on hands‑on experience; contribute to continuous improvement of team practices.

• 1–2+ years professional experience in network QA, network engineering, or related testing role; recent graduates with substantial internship/project experience may apply if meeting experience minimum.
• BS/MS in Computer Engineering, Computer Science, Computer Networking, Information Security, or related field.
• Strong foundational knowledge of networking principles and practical exposure to campus or data‑center deployment concepts.
• Proficient in Python for automation with demonstrated experience writing production‑quality scripts; good understanding of data structures and clean code practices.
• Hands‑on experience applying AI/ML tools or LLMs to testing, log analysis, or automation is highly desirable.
• Practical awareness of cybersecurity principles and experience incorporating security checks into test plans.
• Effective communicator, collaborative team member, proactive problem‑solver, and comfortable working in hands‑on lab environments.

• 1–2+ years using Python for test automation; familiarity with pytest or similar frameworks, scripting best practices, and version control (Git).
• Working knowledge of networking concepts and protocols: TCP/IP, IPv4/IPv6, OSPF, BGP, VLANs, NAT, DHCP, DNS; hands‑on exposure to VxLAN/EVPN/MPLS is a plus.
• Demonstrated experience collecting and analyzing packet captures and logs (Wireshark, tcpdump) for troubleshooting and RCA.
• Practical experience or coursework using AI models/LLMs for log analysis, test generation, classification, or related tasks.
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