Job Summary

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.

Responsibilities

- 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 Requirement
- 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.

Educational Requirement
- Bachelor of Engineering

Preferred Skills
- Quality Engineering
- Quality Engineering Strategy Design
- Test Scripting
- Verification Validation
- Agent Engineering
- AI/ML Solution Architecture and Design
- LLMOps
- Generative AI
- ETL Data Quality

Service Line

Infosys Quality Engineering

📌 AI Quality engineering Lead (Bengaluru)
🏢 Infosys
📍 Bengaluru