We are looking for a seasoned and forward-thinking Head of Quality Engineering to lead and transform our quality practices at scale. This role oversees a team of 60+ QA Engineers and SDETs and is accountable for embedding quality as a core engineering principle across the organization.
Responsibilities
- AI Transformation Roadmap: Define and execute strategy to transition from automated testing to AI-augmented Quality Engineering, utilizing LLMs and ML to shrink the feedback loop.
- Quality Governance: Establish global quality standards and Golden Paths for AI integration, ensuring that AI-generated tests meet security, compliance, and performance benchmarks.
- Operational Excellence: Redesign the QA process to be AI-First, moving away from manual gatekeeping toward continuous, predictive validation within the CI/CD pipeline.
- Autonomous Framework Orchestration: Oversee the development of self-healing test architectures and AI-driven visual regression systems that adapt to UI changes without human intervention.
- Predictive Risk Analysis: Deploy machine learning models to analyze code churn and historical defect data, enabling the team to predict hotspots and allocate testing resources proactively.
- Synthetic Data Management: Lead the implementation of AI-generated synthetic data strategies to ensure high-fidelity testing environments that respect data privacy and compliance.
- Upskilling & Mentorship: Lead the cultural shift from Manual/Automation QA to Quality Software Engineering, training the team on AI literacy, prompt engineering, and ML model evaluation.
- Cross-Functional Advocacy: Partner with VPs of Engineering and Product to embed quality earlier in the lifecycle (Shift-Left) and utilize production signals for testing (Shift-Right).
Job Requirements
- B.S./M.S. in Computer Science, Engineering, or a related field (or equivalent professional experience in high-growth tech environments).
- 10+ years of experience in Quality Engineering, with at least 5 years in a senior leadership capacity (Director or above) over large-scale automation environments.
- Proven experience or deep conceptual knowledge of integrating Large Language Models (LLMs) into the SDLC (e.g., using OpenAI API, LangChain, or vector databases for test case synthesis).
- Hands-on engineering leadership - must be able to read code, debug a CI pipeline live, and architect at the system-detail level. Not a process-only or deck-driven leader.
- Strong command of modern CI/CD tools (GitHub Actions, GitLab CI), infrastructure as code (Terraform, Kubernetes), and high-level programming (Python, Go, or Java).
- Demonstrated success leading an organization through a major technological pivot (e.g., Cloud migration, DevOps adoption, or AI integration).
- Ability to define and track North Star metrics such as Mean Time to Detect (MTTD), AI-driven ROI, and Defect Escape Rate (DER).
