AI-Native Quality Engineering Lead

AI QA Lead

Remote

$60-63/hr on C2C max

The AI-Native Quality Engineering Lead will own AI adoption, governance, and quality assurance across the end-to-end Quality Engineering lifecycle.

The role focuses on Agentic AI, GenAI, LLM, RAG, specification-driven testing, automation, validation, governance, and release assurance.

Key Responsibilities
Lead AI-assisted requirement analysis, risk identification, acceptance-criteria definition, and specification-driven test design.
Drive AI-based generation of positive, negative, edge-case, and business-rule-driven test scenarios aligned to customer requirements.
Govern LLM, RAG, and agentic AI validation, including model behaviour checks, hallucination validation, data quality, traceability, and responsible-AI controls.
Guide automation-ready assets, self-healing automation frameworks, intelligent CI/CD orchestration, quality gates, and audit-ready reporting.
Define measurable QE outcomes across test coverage, defect leakage, automation ROI, release confidence, and delivery efficiency.
Engage with customers, engineering, architecture, DevOps, security, compliance, and leadership stakeholders to deliver business-aligned QE outcomes.

Experience and Qualifications
Minimum 15 years of overall Quality Engineering experience is mandatory.
Strong experience in QE strategy, test automation, customer-facing solution leadership, stakeholder management, and cross-functional delivery.
Practical exposure to GenAI, LLM, RAG, agentic AI systems, AI evaluation, prompt engineering, or AI-enabled test automation.
Experience with agile delivery, CI/CD practices, test governance, quality metrics, and enterprise SDLC delivery is expected.
Bachelor's degree in computer science, Information Technology, Engineering, or a related technical discipline is mandatory; master's degree and relevant QE, cloud, automation, or AI certifications are preferred.
Strong executive communication, mentoring capability, governance discipline, and ability to deliver reliable, auditable, business-aligned AI Quality Engineering outcomes.