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)Robust 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

📌 AI Strategic Consultant (Bengaluru)
🏢 Infosys
📍 Bengaluru