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NAP Network Test Automation Development Manager

Job Description - NAP Network Test Automation Development Manager

Description

The Network Test Automation Engineering Manager leads and drives the test automation strategy for complex network products and end‑to‑end solutions. This role combines technical depth, people leadership, and strategic ownership to deliver scalable automation frameworks, AI/ML‑driven testing solutions, and continuous quality improvements across carrier‑grade, cloud‑native network platforms.



Responsibilities
  • Lead, mentor, and manage a team of test automation engineers, fostering a culture of innovation, collaboration, and continuous improvement.
  • Own team performance management, including goal setting, feedback, coaching, and career development.
  • Define and execute the test automation strategy and roadmap aligned with product and business objectives.
  • Architect and govern scalable, robust automation frameworks and testing tools for network solutions.
  • Oversee development and execution of automated tests for functional, performance, scalability, and regression testing.
  • Drive integration of test automation into CI/CD pipelines and DevOps workflows.
  • Lead adoption of AI/ML techniques for intelligent testing, anomaly detection, predictive defect analysis, and self‑healing automation.
  • Monitor quality metrics, report automation progress and defect trends, and ensure compliance with quality standards.


Qualifications

You Have:

  • 10+ years of experience, including 3–5 years in a technical leadership or people management role within test automation or quality engineering.
  • Strong expertise in network technologies (IP/MPLS, SDN/NFV, 4G/5G, LTE, Ethernet, Optical and Fixed Networks).
  • Proven experience designing and scaling test automation frameworks using Python, Java, or Go with tools such as Robot Framework, Selenium, and pytest.
  • Solid understanding of CI/CD pipelines and DevOps practices.
  • Hands‑on experience with cloud and container platforms (AWS, Azure, GCP, Docker, Kubernetes).
  • Practical experience or strong understanding of AI/ML applications in testing, data analysis, or network operations.
  • Strong leadership, communication, stakeholder management, and decision‑making skills.
  • Excellent analytical, problem‑solving, and multi‑project management capabilities.


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