AI Strategic Consultant

Job description

**Educational Requirements** Bachelor of Engineering **Service Line** Infosys Quality Engineering **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 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. **Technical and Professional Requirements:** 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). **Preferred Skills:** Technology- Architecture- Architecture - ALL Technology- Infrastructure-Transformation- Infrastructure-Transformation - ALL Foundational- Methodologies- Business Transformation Foundational- Quality Assurance- Quality Assurance Technology- Artificial Intelligence- Artificial Intelligence - ALL Foundational - Artificial Intelligence- Responsible AI by Design Foundational - Strategy- Advisory Skills Technology- Machine Learning- Generative AI- retrieval augmented generation (rag) Foundational- Quality Engineering- Quality Engineering Strategy Design Technology- Agentic AI- Agent Engineering Technology- Agentic AI- AgentOps