AI Strategic Consultant

Bachelor of Engineering

Infosys Quality Engineering

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.

GoodtoHave SkillsThese enhance differentiation and futureproof the role but are not strictly required for core execution:AIdriven quality engineering advisoryGuiding adoption of intelligent testing, predictive risk analytics, and autonomous quality capabilities.AI risk assurance and trust frameworksUnderstanding AI model quality, bias detection, and data quality as QE expands into AIenabled products.Advanced quality intelligence and analytics mindsetLeveraging observability, telemetry, and production insights to influence testing and governance strategies.Innovation and valuerealization focusAbility to distinguish genuine AIdriven lift from vendor hype and steer clients toward pragmatic value outcomes.

Mandatory SkillsThese are essential for baseline success in an enterprise QE advisory leadership role:15+ years of QE experience with enterprise-scale transformation exposureDemonstrated ability to lead and advise large, complex organisations.QE strategy and operating model designDefining multiyear QE roadmaps, governance frameworks, and riskbased quality strategies.Quality economics expertiseCost-of-quality analysis, ROI articulation, and tying QE outcomes to business metrics (cost, speed, resilience).Risk-based and outcome-driven QE leadershipDriving 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).