Senior AI Quality Assurance Lead
1 week ago
About UsWe are a business transformation center dedicated to achieving efficient underwriting operations, utilizing its artificial intelligence capabilities and further advancing its digital transformation and innovation.We will advance our global strategy through pioneering work in streamlined underwriting processes, artificial intelligence, telematics, and analytics, while also providing critical business support functions across underwriting, claims, and operations. Our focus is on setting up a hub for incubation—developing next-generation insurance and technology solutions that will shape the industry's future.Our RoleThe Position involves leading the Quality Assurance and Testing organization within the AI/Data Center of Excellence (CoE), ensuring delivery of reliable, high-performing, and compliant AI and data-driven systems across the enterprise.ResponsibilitiesQuality & Testing Leadership: Establish and lead comprehensive QA strategy across traditional software, data, and AI-driven systems in alignment with enterprise digital transformation goals.Ai Evaluations: Define frameworks and metrics for Ai/Ml testing—covering model accuracy, robustness, fairness, explainability, and bias detection. Develop automated evaluation pipelines for Llms and Ai applications.Test Automation & Innovation: Drive automation-first strategy using Ai-powered testing tools to improve speed, accuracy, and coverage across regression, performance, and integration testing.Mlops & Data Quality: Collaborate with Mlops and data engineering teams to ensure model validation, monitoring, and continuous testing of data pipelines and Ml workflows.Governance & Compliance: Establish quality governance processes for Ai and data systems adhering to internal standards, industry best practices, and regulatory requirements for P&C insurance operations.Cross-Functional Collaboration: Partner with Ai architects, data scientists, software engineers, underwriters, and claims teams to integrate testing early in development and ensure reliable deployment.Ai-in-QA Enablement: Evaluate and deploy Ai-based testing solutions, including intelligent defect prediction, test case generation, and anomaly detection in production systems.Team Development: Build and mentor a high-performing Qa/testing organization specializing in Ai applications, automation, and performance testing.Performance & Reliability: Ensure Ai systems and digital applications meet SLAs for scalability, reliability, interpretability, and compliance.Stakeholder Management: Collaborate with business, technology, and innovation leaders to drive quality culture, risk mitigation, and continuous improvement.
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