NAP Network Test Automation Development Engineer
Summary
Develops and maintains automated testing frameworks for carrier-grade network systems using Python/Java/Go, CI/CD, and AI/ML to ensure reliability and faster releases.
The NAP – Network Test Automation Development Engineer is responsible for building scalable and intelligent test automation solutions for complex network systems. This role blends deep networking expertise with modern automation, CI/CD, and AI/ML techniques to ensure high quality, reliability, and faster delivery of carrier‑grade, cloud‑native network products.
- Design, develop, and maintain scalable automation frameworks for functional, regression, performance, and end‑to‑end network testing.
- Develop automated test suites using Python, Java, or Go with frameworks such as Robot Framework, pytest, and Selenium.
- Integrate automated tests into CI/CD pipelines to enable continuous testing and faster release cycles.
- Apply AI/ML techniques for intelligent test case generation, anomaly detection, predictive defect analysis, and self‑healing automation.
- Analyze large volumes of test and network data to identify patterns, trends, and quality risks.
- Develop AI‑driven tools for network test data analysis, insights, and visualization.
- Collaborate with development, product, and QA teams to ensure testability and timely defect resolution.
- Contribute to test strategy, code reviews, design discussions, and maintain automation documentation.
You have:
- 7+ years of experience with strong programming skills in Python, Java, or Go for test automation and tool development.
- Hands‑on experience with test automation frameworks such as Robot Framework, pytest, Selenium, and Postman.
- Solid understanding of network protocols and architectures (TCP/IP, IP/MPLS, Routing & Switching, SDN/NFV, 4G/5G).
- Experience with CI/CD tools and DevOps practices (Jenkins, GitLab CI, or similar).
- Practical understanding of AI/ML concepts applied to automation or data analysis.
- Exposure to ML libraries such as TensorFlow, PyTorch, or scikit‑learn.
- Familiarity with cloud platforms and container technologies (AWS/Azure/GCP, Docker, Kubernetes).
- Strong analytical, problem‑solving, communication, and cross‑functional collaboration skills.