Role: Lead the Quality Assurance and Testing organization within the AI/Data Center of Excellence (Co E), ensuring delivery of reliable, high-performing, and compliant AI and data-driven systems across the enterprise.
Location: Noida, India
Key Responsibilities:
Quality & 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.
Required Qualifications:
Education: Bachelor’s/Master’s degree in Computer Science, Information Technology, or related technical discipline. Experience: 12+ years of QA/Testing experience with 5+ years in teams accountability too; proven track record in managing enterprise-scale QA functions, including testing of AI/ML systems. Technical Skills: Expertise in test automation frameworks, performance testing, CI/CD integration, and quality engineering of AI models and APIs. AI Testing Expertise: Experience designing AI evaluation strategies for LLMs, NLP systems, computer vision, or predictive models, including use of benchmarks and bias/fairness tools. Tools & Platforms: Proficiency with tools like Selenium, Py Test, Robot Framework, Postman, JMeter, Tensor Board, and AI Eval frameworks (e.g., Prompt Bench, Deep Eval, Lang Smith). Insurance Domain: Understanding of Property & Casualty (P&C) insurance workflows including underwriting, claims, and policy management is preferred. Governance & Compliance: Familiarity with ISO, SOC2, and model validation standards for AI assurance. Communication: Strong ability to communicate technical quality concepts to executive and non-technical stakeholders
Preferred Qualifications: Insurance & AI: Experience testing AI applications in P&C insurance domain, including underwriting automation, claims triage, and telematics. AI in QA: Proven experience integrating AI tools for intelligent automation, defect analysis, and test optimization. Leadership: Strong team-building, mentorship, and stakeholder engagement experience in global organizations. Innovation Focus: Background in developing next-generation QA frameworks for digital transformation and AI assurance
