Lead and transform a 50-person global QA organization by developing traditional and AI testing strategies, directing performance, scalability, security, and resilience testing, allocating resources, building quality ownership, and mentoring engineers in automation, non-functional testing, and AI validation.
About the Role
- We are seeking a Hands-On QA Lead to drive quality across our AI initiatives and lead a team of under 10 QA engineers. This is not a purely administrative management role—you will actively roll up your sleeves, write test strategies, design AI validation methods, and work directly alongside our team on complex AI/ML features.
- You will partner closely with our 2 experienced AI QA engineers to build custom AI testing tools, establish evaluation metrics, and set clear boundaries between traditional and non-deterministic AI testing.
Core Responsibilities
1. Hands-On AI Testing & Strategy (60%)
- Direct AI Participation: Actively join core AI product initiatives to design test strategies, evaluate model responses, and validate data pipelines hands-on.
- Define Test Scopes: Establish clear guidelines differentiating traditional testing (API, UI, functional) from AI testing (hallucinations, model drift, prompt quality, accuracy metrics).
- Build AI Tooling & Infrastructure: Collaborate with existing AI QA engineers to build custom testing tools, synthesize test datasets, and design automated reporting for AI metrics.
2. Team Leadership & Execution (20%)
- Resource Allocation: Assign team members to initiatives, manage capacity, and ensure project milestones are met without burning out the team.
- Mentorship & Process: Establish early bug-prevention processes and upskill traditional QA members into AI and automation testing.
- Cross-Functional Collaboration: Partner directly with Product Managers, Data Scientists, and Developers to refine ambiguous AI requirements early in the discovery phase.
3. Core System & Non-Functional Quality (20%)
- Ensure end-to-end quality standards across API/UI automation, baseline performance, and system reliability alongside AI feature releases.
Requirements
- Hands-On AI Testing Experience: Proven track record of actively testing AI/ML features, building AI test strategies, and defining model performance/accuracy metrics.
- Leadership Experience: 5+ years in QA/Software Engineering, with experience managing or leading small teams (<10 members).
- Tool & Strategy Creation: Experience building testing frameworks, tools, and processes from scratch (not just following existing templates).
- Resource & Capacity Management: Ability to balance resources, estimate effort, and adjust timelines in fast-paced, ambiguous environments.
- Technical Depth: Strong hands-on coding/scripting skills (e.g., Python), deep understanding of API testing, data validation, and AI evaluation techniques.
- Communication: Excellent spoken and written English to align engineering, data, and product teams on technical QA strategies.
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