Simulate and test full-scale network architectures with AI/ML correlation engines using multiple network operating systems via WebUI, REST APIs, CLI, and shell interfaces.
Ensure network topology discovery and anomaly detection configurations match real-world multi-vendor physical setups.
Test real-time syslog processing, NLP-based pattern recognition, and predictive analytics across complex network topologies.
Review architectural designs and requirements for new features including machine learning algorithms and correlation engines.
Design and implement end-to-end tests for new features, including GA and maintenance releases.
Validate integration with multi-cloud environments and IT Service Management (ITSM) tools.
Report and document bugs, assist in reproducing and debugging issues, verify R&D bug fixes, and raise unresolved issues.
Perform various system tests (regression, performance, functional, security, etc.).
Design, implement, and maintain automated tests and framework APIs in Python.
Requirements
Practical/BA or equivalent experience in Computer Science, Computer Engineering, Electrical Engineering, or related field.
8+ years of hands-on experience in QA and/or Networking.
2+ years of experience testing network monitoring, AIOps, or enterprise networking solutions.
Solid networking knowledge and hands-on experience with switching and routing.
Strong automation/scripting skills in Python and Linux Bash.
Proficiency in Python OOP and experience writing automation for WebUI using Selenium/Playwright.
Experience with API testing tools and methodologies (REST, JSON, HTTP).
Ability to handle multiple tasks, prioritize effectively, and completing duties with minimal supervision.
Dedicated, able to take ownership of tasks in a fast-paced AI development environment.
Confident and effective verbal and written communication skills in English for collaboration with global teams.
Strong CCNA and/or ISTQB, or similar certifications demonstrating networking and testing expertise.
Sophisticated experience with Python automation and source control tools (Git/SVN) for enterprise-scale projects.
Experience with CI/CD tools (Jenkins, GitLab).
In-depth Linux and Linux kernel knowledge (LPIC-2 level).
Experience with virtualization and containerization environments (Linux KVM, VMware, ESXi, Docker, Kubernetes).
Benefits
NVIDIA is committed to fostering a diverse work environment and is proud to be an equal opportunity employer.
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