Nokia Solutions and Networks India (P)Ltd
NAP Network Test Automation Development Engineer
Skills
2 months ago
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Important information
Contract type:
Permanent
Location:
Bengaluru, Karnataka, India
Work mode:
Onsite
Published on:
31 August 2026
What they need
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 carriergrade, cloudnative network products.
HOW YOU WILL CONTRIBUTE AND WHAT YOU WILL LEARN
- Design, develop, and maintain scalable automation frameworks for functional, regression, performance, and endtoend 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 selfhealing automation.
- Analyze large volumes of test and network data to identify patterns, trends, and quality risks.
- Develop AIdriven 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:
- 7+ years of experience with strong programming skills in Python, Java, or Go for test automation and tool development.
- Handson 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 scikitlearn.
- Familiarity with cloud platforms and container technologies (AWS/Azure/GCP, Docker, Kubernetes).
- Strong analytical, problemsolving, communication, and crossfunctional collaboration skills.
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