Key Responsibilities:

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

Technical Requirements:

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

Preferred Skills:

Foundational->Quality Engineering->Quality Engineering Strategy Design,Foundational->Quality Engineering->Test Scripting,Foundational->Quality Engineering->Verification & Validation,Technology->AI-Agentic AI->AgentOps,Technology->AI-AI Engineering->AI/ML Solution Architecture and Design,Technology->AI-AI Engineering->LLMOps,Technology->AI-AI Engineering->MLOps,Technology->AI-Generative AI->Generative AI - Basic,Technology->AI-Responsible AI->Responsible AI->explainable ai,Technology->AI-Generative AI->Artificial Intelligence - BASIC,Technology->ETL & Data Quality->ETL & Data Quality - ALL

AI Quality engineering Lead