In an increasingly connected world, the pandemic has highlighted just how essential telecom networks are to keeping society running. The Network Infrastructure group is at the heart of a revolution to connectivity, pushing the boundaries to deliver more and faster network capacity to people worldwide through our ambition, innovation, and technical expertise

Join Optical Networks division, where innovation meets scale in the AI-driven data center era. With the recent acquisition of Infinera, we’ve united two industry leaders to create an optical networking powerhouse—combining cutting-edge technology with proven leadership to redefine the future of connectivity.

The Quality Assurance Engineer is a core member of a cross‑functional Scrum team, collaborating closely with developers, product owners, and system architects to ensure that quality is embedded in the product from the earliest stages. Working in a multicultural environment, the role contributes directly to the delivery of high‑quality Nokia advanced optical networking solutions.

• Solid understanding of optical transmission fundamentals

• Proficiency in Python-based test automation, including pytest test design and integration into CI/CD environments

• Knowledge or hands‑on experience with AI/ML applications related to software quality, such as:AI‑assisted test automation tools

• Data analysis for defect detection or interpretation of test results

• AI-driven techniques to expand test coverage or improve testing efficiency

• MSc or BSc in Computer Science, Telecommunications, Physics, or a related technical discipline

• Fluency in English, both written and spoken

• ISTQB certification is considered an asset

• Strong interest in leveraging Artificial Intelligence to enhance software quality and testing efficiency

• Strong sense of ownership, responsibility, and accountability

• Effective communication skills with the ability to share knowledge and collaborate across teams

• Excellent troubleshooting and problem‑solving abilities

The position covers the full Product R&D lifecycle, including:

• Active participation in Agile ceremonies such as sprint planning, backlog refinement, daily scrums, reviews, and retrospectives—bringing a strong quality mindset to requirement analysis, design discussions, acceptance criteria definition, early risk identification, and overall testability.
• Design, development, and execution of automated tests using Python and pytest, fully aligned with the team’s Definition of Done.
• Application of AI‑assisted testing techniques, including LLM‑supported test design, automated test generation, synthetic test data creation, and failure pattern analysis to enhance coverage, regression efficiency, and feedback speed.
• Continuous improvement of test strategies and QA practices, including contributions to automation frameworks and modernization of Agile quality processes.