Job Description & How to Apply Below
The Lead AI SDET provides technical leadership for quality engineering across multiple Scrum teams or a major product area. This role ensures AI products are reliable, scalable, and meet quality standards through strong automation, evaluation practices, and cross-team coordination. The role focuses on setting quality direction, mentoring teams, and working closely with Engineering and Product teams to deliver high-quality AI solutions.
Responsibilities
AI Quality Leadership
Lead quality engineering efforts across multiple teams or a large product area.
Align testing strategy, quality standards, and release readiness across teams.
Plan evaluation coverage and ensure risks are identified and managed early.
AI Evaluation & Quality Governance
Define and maintain evaluation standards and regression checks for critical product features.
Run quality reviews and release readiness checks.
Work with Security, Privacy, and Product teams to ensure AI compliance and responsible AI practices.
Automation & Platform Enablement
Standardize automation frameworks and tools across teams.
Improve CI/CD pipelines by reducing test failures and improving feedback speed.
Enhance monitoring and reliability of test environments.
Mentorship & Quality Culture
Mentor SDETs and engineers across teams.
Promote consistent QA practices and continuous improvement.
Support teams with best practices and technical guidance.
Additional Responsibilities
Perform other related duties as required.
Qualifications
Required
Bachelor’s degree in Computer Science, Engineering, or equivalent experience.
10+ years of experience in QA, SDET, or automation roles, including leadership across teams.
Strong programming and automation framework experience.
Experience building scalable test and evaluation infrastructure.
Experience improving CI/CD pipeline reliability.
Hands-on experience testing AI systems such as LLMs, RAG systems, or AI agents.
Ability to mentor teams and drive alignment in Agile/Scrum environments.
Preferred
Experience supporting multiple teams through shared platforms or internal tools.
Experience with monitoring and reliability practices for AI systems.
Experience working in enterprise or regulated environments.
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