1. Quality Assurance for AI/ML Models and Pipelines 

• Develop and execute comprehensive test plans, test cases, and test scripts to validate AI/ML models and data pipelines. 

• Perform rigorous testing of machine learning models to ensure accuracy, reliability, and robustness under various scenarios. 

• Validate data preprocessing, feature engineering, and model training pipelines for correctness and consistency. 

• Identify and address performance bottlenecks in AI systems, ensuring scalability for large datasets and real-time applications. 

• Collaborate with data scientists to validate model outputs and metrics against business requirements. 

2. Automation Testing and Tool Development 

• Design and implement automated test frameworks and tools tailored for AI/ML workflows. 

• Automate testing of model deployments, APIs, and data pipelines using industry-standard tools and frameworks. 

• Create scripts to simulate edge cases, stress conditions, and user interactions for AI systems. 

• Build monitoring tools to assess AI model drift, data inconsistencies, and system performance post-deployment. 

• Continuously enhance automation coverage and testing efficiency through innovative practices. 

3. Collaboration and Cross-Functional Engagement 

• Work closely with software engineers, data engineers, and product managers to align QA strategies with project goals. 

• Participate in code reviews, design discussions, and sprint planning to incorporate QA perspectives early in the development lifecycle. 

• Provide actionable feedback and insights to development teams to resolve issues and improve system quality. 

• Support end-to-end integration testing of AI/ML solutions across multiple platforms and systems. 

• Act as a quality advocate, promoting best practices for testing and validation within the organization. 

4. Governance, Compliance, and Reporting 

• Ensure compliance with data privacy, security, and ethical AI standards during testing and deployment. 

• Develop and maintain comprehensive documentation for QA processes, test cases, and system validations. 

• Monitor and report on key QA metrics, including defect rates, coverage, and system reliability. 

• Support regulatory audits and reviews by providing required testing documentation and evidence. 

• Stay up-to-date with industry trends, tools, and practices in QA for AI/ML systems. 

5. Continuous Improvement and Innovation 

• Research and adopt emerging technologies and frameworks for AI/ML testing and validation. 

• Drive continuous improvement initiatives to enhance the efficiency and effectiveness of QA processes. 

• Proactively identify and resolve quality gaps in AI/ML workflows, ensuring a seamless user experience. 

• Contribute to building a culture of quality and accountability within the AI/ML team. 

• Mentor junior team members on QA best practices and technical skills. 

Qualifications & Experiences: 

Academic Qualifications: 

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or a related field. 

• Certifications in software testing (e.g., ISTQB, CSTE) or machine learning (e.g., AWS ML, TensorFlow Developer) are a plus. 

Professional Experience: 

• 3+ years of experience in QA engineering, with at least 1+ years focused on testing AI/ML systems. 

• Proven&n Official notification