Director of Quality

Department Technology, Baxter Planning

Reports To Chief Technology Officer

Team QA Manager, QA engineers, test automation engineers

This role owns quality strategy across all Baxter Planning products and leads the modernization of the Quality organization. Under these conditions manual review does not scale, and automation without a test strategy creates false confidence. The Director of Quality will move quality ownership earlier in the development lifecycle, reposition the QA team toward quality engineering, standards and platform work, upskill the team, and adopt or build AI-native quality tooling. Own quality strategy across all Baxter products.

Define the target operating model for the Quality organization, including how quality ownership is split between product engineering teams and a central quality engineering and enablement function.

Measure effectiveness with methods such as mutation testing, not coverage alone.

Define test architecture standards per product, prioritized by business risk and customer impact.

Evaluate and adopt AI-native quality tools and agents across the lifecycle, from requirements to production observability, and build custom ones where commercial options do not fit.

Design quality controls that scale with AI-generated code, including independent verification of AI-generated tests.

Define the testing and evaluation approach for agentic and LLM-based product features, including non-deterministic outputs and regression on model, prompt and context changes.

Define and own CI/CD quality gates with the Development leaders and the Director of IT/DevOps & Security, and a shared release readiness model with Engineering and Product.

Define quality metrics and report them to leadership on a regular cadence.

Product engineering teams own the quality of what they build, and the Quality team sets the bar through standards,



tooling and test strategy review.

The Quality team spends most of its time on quality engineering and tooling, and little on manual execution.

AI-generated code and AI product features pass through verification that does not depend on manual review of every change.

Engineering teams see quality gates as useful, and quality signals are trusted by engineering leadership and Product.

Talent: Hire, Grow, Retain

Moves the team from manual execution toward test strategy, automation design, tooling development, and building and supervising AI agents.

Defines role profiles and career paths for quality engineers, and contributes to 9-box reviews.

Partners with Product Engineering directors to include quality practices in developer onboarding.

CTO Customers reporting quality issues or escalations, as needed

Director(s) of Product Engineering and Data AI and testing tooling vendors

Director of IT/DevOps & Security Customer technical teams validating fixes, occasionally

Product Management

8+ years in quality engineering or test automation, including 3+ years leading a QA or quality engineering team.

- Experience in software product companies, owning quality outcomes for products in production.
- Hands-on experience with agentic development tools such as Claude Code or Cursor, and a practical understanding of how agents work: instructions, context, tool use and MCP, and their failure modes.
- Experience designing test strategy and test architecture,



including measuring test effectiveness beyond coverage.
- A track record of evaluating AI quality tooling critically, including identifying weak or redundant AI-generated tests.
- Hands-on experience with a up-to-date automation stack (for example Playwright or Selenium), CI/CD quality gates, and release go/no-go decisions.
- Experience reorganizing a QA team: changing roles, coaching engineers from manual execution toward engineering work, hiring and career pathing.
- Experience in a metrics-driven environment, such as a PE-backed company, with regular quality reporting to leadership.
- Nice to have: prior experience leading the transformation of a QA organization toward AI-native, development-integrated quality engineering.
- Nice to have: experience testing or evaluating LLM-based and agentic product features.
- Nice to have: experience building internal quality tooling used by other engineering teams.

AI-native mindset Default response to a manual, repetitive process is to ask how to automate it or use AI, rather than how to staff it.

Rigor Does not treat a green build as evidence of quality. Pragmatic risk judgment Knows where deep coverage is needed (revenue-critical paths, complex business logic, AI features) and where lighter testing is enough.

Critical evaluation of AI tooling Stays current on AI-assisted quality tools and evaluates them on evidence, including their failure modes.

Systems and platform thinking Improves quality through standards, tooling and process design that scale across teams, rather than through individual test cases.

Cross-team influence Gets engineers who do not report to them to write better tests and own quality, through credibility rather than mandate.

Data-driven Uses defect trends, escape rates and production signals to set priorities.

📌 QA / Quality Manager (India)
🏢 Baxter Planning
📍 India