Key Responsibilities: ------------------------- * Lead end to end enterprise QE transformation by assessing current capabilities benchmarking maturity and defining AI first target state blueprints across people process and technology * Design intelligent governance and operating models that embed predictive autonomous quality practices aligned to business outcomes while driving rapid value through early wins * Enable sustainable change through executive alignment change management transition and knowledge transfer strategies and reduced dependency on consulting support * Additionally support growth through C suite advisory pre sales leadership creation of proprietary IP and market shaping via thought leadership and industry engagement Technical Requirements: --------------------------- * Mandatory Skills * These are essential for baseline success in an enterprise QE advisory leadership role * 15 years of QE experience with enterprise scale transformation exposure * Demonstrated ability to lead and advise large complex organisations * QE strategy and operating model design * Defining multi year QE roadmaps governance frameworks and risk based quality strategies * Quality economics expertise * Cost of quality analysis ROI articulation and tying QE outcomes to business metrics cost speed resilience * Risk based and outcome driven QE leadership * Driving measurable improvements in defect leakage release velocity reliability and compliance * Modern engineering fluency at advisory level * Strong understanding of DevOps CI CD cloud native and platform engineering concepts to translate technical complexity into actionable quality guidance without hands on pipeline work Additional Responsibilities: -------------------------------- * Good to Have Skills * These enhance differentiation and future proof the role but are not strictly required for core execution * AI driven quality engineering advisory * Guiding adoption of intelligent testing predictive risk analytics and autonomous quality capabilities * AI risk assurance and trust frameworks * Understanding AI model quality bias detection and data quality as QE expands into AI enabled products * Advanced quality intelligence and analytics mindset * Leveraging observability telemetry and production insights to influence testing and governance strategies * Innovation and value realization focus * Ability to distinguish genuine AI driven lift from vendor hype and steer clients toward pragmatic value outcomes Preferred Skills: --------------------- Foundational ->Artificial Intelligence->Responsible AI by Design,Technology->AI-AI Engineering->AI/ML Solution Architecture and Design,Technology->AI-Generative AI->Artificial Intelligence - BASIC,Technology->AI-Generative AI->Generative AI - Basic->retrieval augmented generation (rag),Technology->AI-Responsible AI->Responsible AI->explainable ai,Technology->API Testing->RestAssured,Technology->Architecture->Architecture - ALL,Technology->Mobile Automation Testing->Mobile Test Automation process,Technology->Mobile Testing->BDD, TDD, ATDD for Mobile Test Automation