IN
Job Description
Key Responsibilities:
- Oversee comprehensive enterprise Quality Engineering (QE) transformation by evaluating existing capabilities, benchmarking maturity levels, and establishing AI-first target state blueprints encompassing people, processes, and technology.
- Create intelligent governance and operational frameworks that incorporate predictive autonomous quality practices aligned with business objectives while facilitating swift value realization through initial successes.
- Drive sustainable change through executive alignment, change management strategies, transition efforts, and knowledge transfer methodologies, minimizing reliance on consulting services.
- Additionally, foster growth by providing C-suite advisory support, leading pre-sales initiatives, developing proprietary intellectual property, and influencing market trends through thought leadership and industry collaboration.
Technical Requirements:
- Mandatory Skills
- These skills are crucial for achieving baseline success in an enterprise QE advisory leadership position.
- A minimum of 15 years of experience in QE, with a strong background in enterprise-scale transformations.
- Proven capability to lead and advise large, complex organizations.
- Expertise in designing QE strategy and operational models.
- Development of multi-year QE roadmaps, governance frameworks, and risk-based quality strategies.
- In-depth knowledge of quality economics.
- Ability to conduct cost of quality analyses, articulate ROI, and connect QE outcomes to business metrics such as cost, speed, and resilience.
- Leadership in risk-based and outcome-driven QE practices.
- Deliver measurable enhancements in defect leakage, release velocity, reliability, and compliance.
- Fluency in modern engineering concepts at an advisory level.
- Solid understanding of DevOps, CI/CD, cloud-native, and platform engineering principles to translate technical complexities into actionable quality guidance without engaging in hands-on pipeline tasks.
Additional Responsibilities:
- Good to Have Skills
- These skills can differentiate and future-proof the role but are not mandatory for essential execution.
- Advisory on AI-driven quality engineering.
- Facilitating the adoption of intelligent testing, predictive risk analytics, and autonomous quality capabilities.
- Experience with AI risk assurance and trust frameworks.
- Knowledge of AI model quality, bias detection, and data quality as QE evolves into AI-enabled products.
- Advanced mindset in quality intelligence and analytics.
- Utilizing observability, telemetry, and production insights to shape testing and governance strategies.
- Focus on innovation and value realization.
- Capability to differentiate authentic AI-driven enhancements from vendor exaggerations and guide clients toward practical value outcomes.