V&V Engineer

V&V Engineer in AI/ML team will be responsible for defining, executing, maintaining verification and validation activities for AI/ML-enabled software solutions used in commercial products and internal processes. V&V Engineer will work closely with software engineers, data scientists, product managers, and domain experts to ensure solutions are robust, reliable, scalable, and compliant with quality expectations. This role is essential to ensuring that AI/ML systems meet functional, performance, and business requirements before deployment.

Main Responsibilities:

• Define and implement verification and validation strategies for AI/ML software solutions across the development lifecycle.
• Collaborate with software engineers and data scientists to understand system architecture, model behaviour, and technical requirements to develop effective test approaches.
• Work closely with cross-functional teams to translate business and product requirements into validation plans, test cases, and acceptance criteria.
• Develop, execute, and maintain test protocols covering functional, integration, system, regression, and performance testing.
• Validate AI/ML models and software components against expected behaviour, accuracy, robustness, and operational requirements.
• Contribute to the development of internal tools, test frameworks, and automated validation processes to improve efficiency and repeatability.
• Support root cause analysis and problem resolution for defects identified during development, integration, and product introduction.
• Stay up to date with the latest trends and technologies in software quality assurance, AI/ML validation, and test automation.
• Support Senior Engineers to safeguard design qualities through rigorous verification and validation activities.
• Maintain an active role in product introduction, cost improvements, schedule adherence, and problem resolution to meet business needs.
• Ensure adherence to engineering development methodologies, including Agile and the New Product Introduction (NPI) / OTR processes.
• Maintain high standards of test quality, traceability, and documentation, ensuring requirements are properly verified and validated.
• Participate in technical reviews and contribute to the definition of validation criteria, test strategies, and release readiness assessments.
• Implement best practices for software verification, test automation, defect tracking, and validation reporting.
• Develop technical competencies by contributing to structured test and validation methodologies, standards, and best practices for AI/ML software.
• Support the creation and continuous improvement of reusable test assets, validation templates, and documentation standards.
• Foster a collaborative and inclusive team environment. Encourage continuous learning and development within the team.
• Support knowledge sharing across engineering teams on verification, validation, quality, and release readiness practices.

Qualifications & Requirements:

• Bachelor or Master of Science in Computer Science, Software Engineering, Electrical Engineering, Systems Engineering, Mathematics, or related field.
• Minimum 3-5 years of relevant experience in software verification, validation, quality assurance, or test engineering in an industrial or technology environment.
• Strong understanding of software development lifecycle, testing methodologies, and defect management processes.
• Experience with test planning, test execution, test automation, and requirements traceability.
• Knowledge of Agile development methodologies and New Product Introduction (NPI) processes.
• Strong analytical and problem-solving skills with ability to investigate, document, and resolve technical issues.
• Strong oral and written communication skills.
• Experience validating AI/ML-enabled software systems, including model performance, data quality, and robustness considerations.
• Experience with scripting or programming languages such as Python.
• Experience with test automation frameworks and CI/CD environments.
• Familiarity with cloud environments such as AWS or Azure.
• Ability to work across organizations in a matrix environment.
• Experience with requirement management and defect tracking tools.
• Strong interpersonal skills and ability to collaborate with software engineers, data scientists, and business stakeholders.