AI Strategic Consultant

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.

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.

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 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).

Preferred Skills

• Responsible AI by Design
• generative ai
• retrieval augmented generation (rag)
• explainable ai
• RestAssured
• Architecture – ALL
• Artificial Intelligence – ALL
• Automated Testing – ALL
• Test automation framework design
• Digital Architecture
• Mobile Test Automation process
• BDD
• TDD
• ATDD for Mobile Test Automation

Educational Requirements

Bachelor of Engineering