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.
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
Requirements
Experience in MarTech / AdTech ecosystems
Familiarity with healthcare data, compliance, and constraints
Exposure to building or testing AI-first or non-deterministic systems at scale
Company Summary
Learn more about Zeta: https://zetaglobal.com/press-news/
Zetaglobal · Bengaluru, India