Role & responsibilities :

1. Quality Assurance Leadership (70%) :

- Define and implement a comprehensive QA strategy aligned with organizational delivery goals, embedding automation-first and AI-enabled practices.

- Standardize QA methodologies, processes, and quality benchmarks across delivery streams.

- Oversee end-to-end QA lifecycle including test planning, execution, defect management, and reporting.

- Implement QA governance through periodic audits, reviews, and process adherence checks.

- Drive adoption of AI/GenAI-enabled QA practices, including test case generation, defect prediction, test optimization, and automated documentation.

- Evaluate and implement modern QA tools such as self-healing automation frameworks and AI driven testing platforms.

- Build, mentor, and lead a high-performing QA team with focus on automation and AI capability development.

- Strategize AI usage in all Quality Assurance related processes.

- Drive automation Quality Assurance.

- Preparing the Quality dashboards and publishing them to Senior Management.

- Strengthen stakeholder collaboration across Delivery, PMO, Business, and Technology teams.

- Proactively identify risks and drive mitigation plans using data-driven insights.

- Track QA metrics and continuously improve quality and productivity.

2. Delivery Assurance Framework (30%) :

- Design and implement a scalable Delivery Assurance framework to ensure consistent and predictable outcomes.

- Establish standardized processes and governance mechanisms across delivery teams. Define and enforce compliance and quality standards across projects.

- Strategize and implement the usage of AI to improve Delivery Processes.

- Build delivery dashboards and KPIs to enable performance monitoring and decision-making.

- Introduce data-driven and predictive insights into delivery health tracking.

- Conduct governance reviews and audits to ensure adherence to frameworks.

- Analyze delivery trends and drive continuous improvement initiatives.

- Ensure a Quality mindset in the teams; work towards a better FCR and sustained Knowledge Retention among teams.

- Continuously improve the factors that strengthen delivery assurance.

Key Skills & Competencies :

Quality Engineering & QA :

- Strong experience in QA methodologies, test strategy, and automation practices.

- Experience transitioning from traditional QA to quality engineering practices.

AI Mindset in QA (Strong Exposure Preferred) :

- Working knowledge of AI/ML concepts relevant to testing.

- Exposure to AI-enabled QA tools or GenAI use cases such as test case generation and analysis.

- Ability to identify practical AI use cases in QA and drive adoption.

- Strong inclination and capability to ramp up in AI-driven QA practices.

Leadership & Collaboration :

- Proven ability to lead QA teams in complex delivery environments.

- Strong stakeholder management and influencing skills.

- Ability to drive cross-functional adoption of quality practices.

Analytical & Execution :

- Strong analytical and problem-solving skills.

- Data-driven decision-making mindset.