AI Quality engineering Lead
Infosys
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.
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