Director of Quality in at Baxter Planning

BAXTER PLANNING
Director of Quality
QA Strategy & Test Automation — Technology

Department	Technology — Baxter Planning
Reports To	Chief Technology Officer
Team	QA Manager, QA engineers, test automation engineers
Location	Pune, India
Employment Type	FTE

This Role owns quality across all of Baxter Planning’s products — not as a downstream gate, but as an engineered, largely automated discipline built into the SDLC. Given Baxter’s legacy defect backlog and the current absence of automated regression coverage, this role is central to derisking the company’s modernization effort and enabling confident, fast delivery. We're looking for a Director of Quality who will leverage AI-assisted testing and quality tooling aggressively — test generation, mutation testing, visual regression, production anomaly detection — rather than scaling quality through headcount alone.

Context: Baxter Planning is a PE-backed (Marlin Equity) service supply chain planning SaaS company.

• Own quality strategy and automated test architecture across Prophet (legacy Java 8/SOAP/4GL), Lynx, AI Agents, Data Platform, and Analytics.
• Build and scale automated regression coverage where none exists today, prioritized by risk and customer impact.
• Evaluate and adopt AI-assisted quality tooling — AI-driven test generation, self-healing test suites, mutation testing, visual/UI regression, log- and production-anomaly-based quality signals — to multiply team capacity rather than scaling headcount linearly.
• Define and own quality gates in CI/CD, partnering with the Development leaders/Director of IT/DevOps & Security to embed automated checks directly into the deployment pipeline.
• Partner with Product Engineering directors and Architecture to shift quality left — testability as a design requirement, not an afterthought.
• Own quality metrics and reporting (defect escape rate, coverage, MTTR on quality issues, regression suite health) as an ongoing operating discipline.
• Drive triage and burn-down of the legacy defect backlog, with clear criteria distinguishing ‘fix now’ from ‘won’t fix.’
• Champion a quality culture across engineering — quality as everyone’s job, not just QA’s.

The sustained standard for this role, not a one-time milestone:

• Automated regression coverage keeps expanding and staying healthy — coverage doesn’t quietly rot as the codebase changes.
• AI-assisted testing tools are actively reducing manual test-writing and maintenance burden, freeing the team to focus on judgment-heavy testing (exploratory, edge cases, customer scenarios).
• Defect escape rate to production trends down consistently, not just after an isolated push.
• The legacy defect backlog keeps shrinking as a matter of routine, not periodic cleanup sprints.
• Engineering teams treat quality gates as a help, not a blocker — release confidence stays high without heroic manual QA effort.
• Quality signals (test results, coverage, production anomalies) are visible and trusted by engineering leadership and Product.

• Hires and builds a QA/test automation team strong in both traditional test engineering and AI-assisted tooling — an evolving skill set, not a static one.
• Grows QA engineers toward automation and tooling fluency, shifting the team’s center of gravity from manual execution to automation design and AI tool orchestration.
• Partners with Product Engineering directors so quality ownership is shared, not siloed — embeds quality thinking into developer onboarding.
• Contributes to 9-box review and career pathing for QA/test engineers.

Internal	External
CTO Director(s) of Product Engineering and Data Director of IT/DevOps & Security Architecture Product Management CX / Support (defect and escalation signal)	Customers reporting quality issues or escalations, as needed AI/testing tooling vendors Customer technical teams validating fixes, occasionally

• 10+ years in quality engineering / test automation, including 5+ years leading a QA or test automation team.
• Direct experience building automated regression coverage into a legacy codebase with limited existing test coverage — not just greenfield test architecture.
• Hands-on familiarity with AI-assisted testing and quality tooling (AI-driven test generation, self-healing tests, mutation testing, visual regression, anomaly detection), and a track record of evaluating tools critically rather than adopting on hype.
• Hands on experience with automation stack - Playwright, Selenium, and CI/CD tooling
• Ownership of release readiness and go/no-go decisions
• Experience owning CI/CD quality gates and partnering with DevOps/Development/platform teams to embed checks into deployment pipelines.
• Comfort operating in a legacy enterprise stack (Java, SOAP, and/or 4GL-era systems) undergoing active modernization.
• Track record of building and scaling a QA organization — hiring, coaching engineers from manual execution toward automation, and career pathing.
• Experience working in a PE-backed or otherwise metrics-driven environment where quality outcomes (defect escape rate, coverage, MTTR) are reported to leadership on a regular cadence.

Automation-First Mindset	Default reaction to a manual, repetitive process is ‘how do we automate or use AI here,’ not ‘how do we staff it.’
Rigor	Doesn’t let a green build create false confidence — verifies the tests are actually testing the right things.
Pragmatic Risk Judgment	Knows where to invest deep coverage (revenue-critical paths, fragile legacy code) versus where lighter-touch testing is enough.
Curiosity About AI Tooling	Stays current on emerging AI-assisted QA and test-generation tools and evaluates them critically rather than adopting on hype.
Cross-Team Influence	Gets engineers who don’t report to them to actually write better tests and own quality — through credibility, not mandate.
Data-Driven	Lets defect trends, escape rates, and production signals — not gut feeling — drive prioritization.