AI Quality Engineering Lead

TEI’s goal is not to build a large traditional QA department. The goal is to build an AI-powered quality engineering capability where AI performs the majority of test creation, execution, regression analysis, and validation.

Your role is to design, govern, and continuously improve that capability.

You are not a traditional QA Lead. You are a Quality Engineering Architect whose expertise goes beyond testing to designing a quality engineering system where AI performs much of the testing and validation.

Your value is in making engineering decisions and governing quality rather than manually executing test cases. As the function matures, you will coach and grow a small team while remaining hands-on.

• Define and own automated release quality gates that determine release readiness.
• Establish quality standards, definitions of done, and acceptance criteria validation across teams.
• Monitor the stability and performance of the shipped product.
• Perform post-sprint validation of work completed by development teams.
• Track acceptance criteria failures back to the responsible teams through an accountability loop.
• Own automated test coverage strategy, execution, and continuous improvement.
• Assess release readiness across performance, regression, and integration.
• Establish baseline QA KPIs and track quality maturity over time.
• Own production incident triage and root cause analysis, ensuring failures do not recur.
• Establish performance benchmarks and regression gates for every release.
• Track client-impacting defects and escape-rate metrics that demonstrate quality improvement.

• Design and govern a quality engineering system in which AI performs testing and you make the engineering decisions.
• Use AI agents to generate and maintain tests, analyze pull requests, detect coverage gaps, identify regressions, and assist with root cause analysis.
• Define standards for AI-assisted test generation and quality governance.
• Build and maintain automated validation pipelines integrated into CI/CD rather than relying on manual test scripts.
• Continuously improve AI-driven test suites based on production signals and escape analysis.
• Operate post-sprint: development teams own quality during development, and you validate what they ship.
• Apply human judgment to determine whether work is truly ready for production and block releases when necessary.
• Report to the Head of Development or AI Engineering Lead and work directly with subject-matter experts and developers across teams.

• Proven QA leadership experience and previous ownership of quality for a product or platform.
• Experience coaching and growing engineers while leading through hands-on work.
• Strong understanding of automated testing strategies, including unit, integration, and end-to-end testing.
• Experience using AI tooling such as Copilot or AI agents for test generation, analysis, or validation.
• Ability to define and enforce quality processes across multiple teams.
• Expertise in performance and stability monitoring.
• Experience integrating automated test suites into CI/CD pipelines.
• Comfort operating autonomously in a remote environment.
• Strong analytical and problem-solving skills.
• Judgment and confidence to block a release when it is not ready, even under deadline pressure.
• Experience with production monitoring, incident response, and root cause analysis.
• A track record of measurably improving quality outcomes such as defect escape rates, release stability, and performance baselines.

• Experience with Angular, .NET, or SQL-based platforms.
• Familiarity with Playwright, Jest, or similar modern test frameworks.
• Background working with SaaS or multi-tenant platforms.
• Experience with release management and deployment governance.
• Relevant certifications in automation or quality engineering.