NAP Network Test Automation Development Engineer
Listed on Nokia’s official careers site. Posted October 16, 2026. Check that it is still open and apply there.
Apply on Nokia’s careers site →Job description
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.
Responsibilities
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.
Qualifications
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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