AI Strategic Consultant

Responsibilities :

Lead endtoend enterprise QE transformation by assessing current capabilities, benchmarking maturity, and defining AIfirst 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 knowledgetransfer strategies, and reduced dependency on consulting support. Additionally, support growth through Csuite advisory, presales leadership, creation of proprietary IP, and market shaping via thought leadership and industry engagement.

Additional Responsibilities:

GoodtoHave Skills These enhance differentiation and futureproof the role but are not strictly required for core execution: AIdriven 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 AIenabled products. Advanced quality intelligence and analytics mindset Leveraging observability, telemetry, and production insights to influence testing and governance strategies. Innovation and valuerealization focus Ability to distinguish genuine AIdriven lift from vendor hype and steer clients toward pragmatic value outcomes.

Technical and Professional 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 multiyear QE roadmaps, governance frameworks, and riskbased 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, cloudnative, and platform engineering concepts to translate technical complexity into actionable quality guidance (without hands-on pipeline work).