Job Description
We are seeking an experienced and detail-oriented QA/QC Engineer with a focus on AI systems to join our team and ensure the quality of our software products and AI-driven applications. The QA/QC Engineer will be responsible for designing and implementing test plans, conducting quality assurance activities, monitoring software development processes, and ensuring that both traditional software and AI systems meet the required quality standards. The ideal candidate will have a strong technical background, excellent analytical skills, and a thorough understanding of software quality assurance, quality control methodologies, and AI quality assurance techniques.
Responsibilities:
- Develop and implement test plans, test cases, and test scripts for various software products and AI-driven systems to ensure they meet the required quality standards.
- Perform functional and non-functional testing for traditional software and AI models, including regression testing, performance testing, and testing for fairness, bias, and interpretability in AI systems.
- Monitor and evaluate software development processes to ensure compliance with industry best practices, company policies, and AI-specific standards (e.g., fairness, transparency, ethical considerations).
- Collaborate with the software development and data science teams to define quality requirements, establish testing strategies for AI algorithms, and ensure the seamless integration of testing activities into the development process.
- Test AI models and machine learning algorithms, validating their accuracy, performance, scalability, and ability to generalize across various datasets.
- Conduct regular quality audits and reviews, identifying areas for improvement in both traditional software and AI-based processes, and recommending corrective actions.
- Track and report on software quality metrics, including AI-specific metrics such as model accuracy, precision, recall, and F1 score, providing regular updates to management on the status of testing activities and product quality.
- Participate in product design reviews, providing input on potential quality risks in software and AI systems, and assisting in the development of mitigation strategies.
- Stay current with industry trends, tools, and technologies related to both software and AI quality assurance, ensuring the application of cutting-edge practices for AI testing, such as adversarial testing, model robustness evaluation, and fairness auditing.
- Assist in the development and implementation of continuous improvement initiatives, focusing on improving the overall efficiency, scalability, and ethical quality of AI-driven systems.
- Validate AI training data quality to ensure it is representative, unbiased, and relevant, thus ensuring model fairness and performance.
Requirements:
- Bachelor's degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, or a related field.
- A minimum of 2 years of experience in software quality assurance and quality control, with a strong focus on AI-based systems and software development environments.
- Strong knowledge of software QA methodologies, tools, and processes, including experience with automated testing tools and AI-specific testing techniques.
- Familiarity with various software development methodologies, such as Agile, Scrum, Waterfall, as well as a strong understanding of AI lifecycle management.
- Experience in testing machine learning models, including evaluating model performance, addressing data issues, and identifying and mitigating algorithmic bias.
- Strong analytical skills to identify AI-specific problems, such as data imbalance, bias, fairness issues, and performance degradation.
- Excellent problem-solving, analytical, and critical thinking skills, especially in troubleshooting AI system performance.
- Strong attention to detail and commitment to ensuring the highest level of software and AI system quality.
- Excellent written and verbal communication skills, with the ability to effectively collaborate with cross-functional teams, including software engineers, data scientists, and product managers.
- Familiarity with software version control systems, such as Git, and issue tracking tools, such as Jira.