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NAP Network Test Automation Development Engineer

Hiring from
India
Work type
Hybrid
Posted
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As Network Test Automation Development Engineer is responsible for designing and developing scalable, intelligent test automation solutions for complex network systems. This role combines strong networking expertise with modern automation, CI/CD, and AI/ML technologies 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:

  • Bachelor degree with 8–12 years of relevant experience. 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).

It would be nice if you also had:

  • 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.

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