Staff QA Automation Engineer

Staff QA Automation Engineer Job Location: Bangalore

About the role

Most teams trade speed for quality. We believe quality is what enables speed. We are building an AI-first engineering organization where teams ship fast, confidently, and continuously. That only works when quality, data, and delivery are designed as a system—not separate functions. This is not a traditional QA role. It is not project management. This is a high-ownership position where you define how an AI-first team builds, validates, and ships production software.  Key Responsibilities: Build Quality as a System Turn quality into developer behavior, not a downstream gate Design automated testing systems that act as guardrails Define and enforce what “production-ready” means through systems Enable teams to anticipate edge cases, failures, and data issues early Eliminate “hope-driven” releases Define What’s Buildable  Partner with Product and Sales to validate ideas early Assess feasibility, data requirements, trade-offs, and cost Translate ambiguity into clear, executable plans Shape viable ideas and stop unworkable ones early Own the Data Reality Map how data actually flows across systems and integrations Identify gaps, inconsistencies, and reliability issues  Ensure features are grounded in real, usable data Navigate internal and third-party data ecosystems confidently Bring Domain Expertise Apply knowledge of MarTech / AdTech (identity, activation, measurement) Operate within healthcare data constraints and compliance requirements Ensure solutions are technically sound and market-relevant Drive Delivery End-to-End Own delivery for AI-first engineering teams Break down work into clear, actionable steps Maintain predictable delivery without slowing velocity Proactively identify risks and adjust early Align Product, Engineering, and GTM teams. Redefine QA for AI Systems Develop testing strategies for AI-generated, non-deterministic systems Validate outputs and behavior, not just code Build evaluation frameworks for AI reliability and accuracy Ensure AI accelerates development without compromising quality What You Need to Succeed  Strong systems thinking across quality, data, and delivery Ability to move from data models → requirements → test strategy → delivery plan Experience improving engineering quality without becoming a bottleneck Confidence pushing back on unclear or unviable ideas Focus on production outcomes, not just releases Core Experience: Background in Quality Engineering, SDET, or technical delivery leadership  Hands-on experience with test automation and CI/CD pipelines Strong SQL skills and ability to work with complex data systems Experience with AI-assisted development or ML/LLM-based systems