AI Quality engineering Lead

AI Quality engineering Lead

Core QE & Solution Architecture 15 + years in Quality Engineering with a progression from hands-on to architecture to advisory; at least 4–5 years designing enterprise-scale QE solutions or leading QE technology practices that serve multiple product lines. Demonstrated ability to architect quality ecosystems — not just frameworks, but end-to-end quality platforms that integrate test intelligence, execution infrastructure, and quality analytics into a cohesive, scalable whole. Track record of driving measurable quality outcomes: cycle-time reduction, coverage expansion, defect-escape elimination — with the business metrics to prove it, not just technical deliverables. Deep understanding of quality measurement systems that connect engineering activity to business indicators — you build dashboards that CTOs present to boards, not test reports that PMs file away. Lead AI-led technology consulting across SRE, DevOps, and Data Intelligence domains — evaluating client quality ecosystems, identifying modernisation opportunities, and producing AI-driven technology strategy and blueprints that embed intelligence into every quality touchpoint from code commit to production monitoring. Drive agentic and LLM strategising and consulting — conducting agentic and LLM evaluation and fitment analyses, designing adoption roadmaps for autonomous quality agents, and advising on how generative AI reshapes test design, defect prediction, and quality workflows. Advise on exponential engineering platforms (Devin, Topaz Fabric QoS, and emerging AI-native tools) — developing Topaz Fabric QoS adoption strategies, evaluating platform fitment, and architecting integration models that amplify engineering productivity at enterprise scale. Lead agentic/AI scalability analyses for quality infrastructure — ensuring test ecosystems scale elastically with release velocity and application complexity, producing scalability analysis reports that quantify capacity constraints and recommend investment priorities. Navigate DTA-led regulatory and compliance advisory — data sovereignty in test environments, auditability of AI-generated artefacts, traceability for regulated industries — translating legal requirements into engineering guardrails that enable innovation within constraints. Deliver contextual engineering consulting — adapting quality strategies to each client's unique technology landscape, organisational culture, and business context rather than applying standardised templates, ensuring recommendations are implementable, not just theoretically sound. Lead rapid-value engagements (4–8 week discovery-to-proof cycles) that demonstrate measurable AI-powered quality outcomes — converting scepticism into investment commitment through quantified business impact. Build the practice's technology point-of-view: vendor-agnostic evaluation frameworks, reference architectures, and adoption playbooks that ensure clients receive best-fit recommendations, not vendor-influenced choices.