Tarun Babbar

I'm a QA Leader with over 18 years of experience building and scaling quality engineering practices across enterprise-scale platforms, most recently at Coupa Software and Varian Medical Systems. Across my career, I've led automation, functional, and performance-testing initiatives that reduced manual testing effort, shortened release cycles, and embedded quality practices into every stage of the development lifecycle — work spanning cross-layer validation (UI, API, and system-level), BDD-driven test design, and CI/CD-integrated automation pipelines.

Over the last few years, my focus has shifted toward reinventing what QA looks like in an AI-native engineering organization. I design and build multi-agent LLM systems for quality engineering — using Retrieval-Augmented Generation (RAG) and the Model Context Protocol (MCP) to connect AI agents to real engineering context such as Jira tickets, TestRail/Zephyr regression suites, and historical bug data. Concrete examples of what I've built include: agents that fetch a Jira bug ticket via MCP and automatically identify related regression tests in TestRail/Zephyr; pipelines that generate test cases directly from Product Requirement Documents (PRDs), turning requirements into structured, executable test coverage; self-healing automation frameworks that adapt to UI/application changes without manual script maintenance; and automated bug-triage systems that classify, prioritize, and route incoming defects with minimal human intervention. I structure how these agents reason using a RICE prompting framework (Role, Instructions, Context, Examples), so the systems I build stay auditable and repeatable rather than being black boxes.

Beyond the technical work, I care deeply about mentoring — I've coached QA engineers and automation specialists on everything from clean test-architecture principles to how to think about AI as a collaborator rather than a replacement in their workflow. I'm comfortable operating at both ends of the spectrum: deep in the weeds of a specific automation framework, and zoomed out, defining a multi-year quality strategy for an engineering org.

What I'm looking for next: a QA leadership or automation-strategy role — ideally at a company that's either actively investing in AI-driven engineering practices or open to someone who can help them get there — where I can own the transformation from traditional QA processes to agentic, AI-native quality engineering, while still bringing the rigor and scalability that 18+ years of enterprise QA experience provides.