Key Responsibilities
Quality Strategy & Leadership
Own the end-to-end quality strategy for PrognoCIS, aligning test coverage, release gates, and quality metrics with R&D and product roadmaps.
Build and lead a multi-disciplinary QA organization spanning functional, automation, performance, security, and AI-testing specialists.
Define and report quality KPIs (defect escape rate, test coverage, MTTR, automation ROI) to R&D and executive stakeholders.
Partner with Engineering, Product Management, and Technical Documentation to embed quality practices earlier in the SDLC (shift-left testing).
Drive a culture of quality ownership across the R&D organization, not just within the QA team.
Test Automation
Architect scalable, maintainable automation frameworks across UI, API, and integration layers (e.g., Selenium, Playwright, Cypress, REST Assured).
Establish automation-first standards for new feature development, targeting high regression-suite coverage with low flake rates.
Integrate automated test suites into CI/CD pipelines for continuous testing and fast feedback loops.
Evaluate and adopt self-healing test frameworks and AI-assisted test generation to reduce automation maintenance overhead.
Define coding standards, code review practices, and reusable component libraries for the automation codebase.
Performance Testing
Own performance, load, and stress testing strategy for a high-volume, multi-tenant healthcare platform (e.g., JMeter, k6, Gatling).
Establish performance baselines and SLAs for key clinical workflows (charting, scheduling, billing, order management) and validate against them each release.
Lead capacity planning and scalability testing in coordination with Engineering and Cloud/Infrastructure teams (AWS).
Institute production-like performance monitoring and proactively identify degradation trends before they impact clinics.
Security Testing
Embed security testing into the release lifecycle, covering OWASP Top 10 risks, authentication/authorization flows, and data-handling paths.
Coordinate vulnerability scanning, penetration testing (internal or third-party), and remediation tracking.
Ensure test coverage for HIPAA-relevant controls — access logging, PHI handling, encryption in transit/at rest — in partnership with Compliance and Engineering.
Maintain a security regression suite that runs continuously against the CI/CD pipeline, not just before major releases.
AI-Augmented & Emerging Test Practices
Evaluate and pilot AI-based test generation, exploratory test assistance, and defect-prediction tooling to increase QA velocity and coverage.
Apply AI-assisted approaches to test-case design for clinical decision-support and AI-driven product features (e.g., PrognoAI / AI Encounter Studio).
Establish testing methodology specific to AI/LLM-driven features — output validation, bias and edge-case testing, prompt-injection and adversarial-input testing, and model-drift monitoring in production.
Stay current on the evolving AI-testing tool landscape and bring in a lightweight evaluation-and-adopt process rather than one-off tool purchases.
Compliance & Healthcare Domain
Ensure QA processes support HIPAA, SOC 2, and relevant regulatory audit requirements, including audit-ready test documentation and traceability.
Maintain test traceability from requirements through to release for regulated clinical and billing workflows.
Partner with Technical Documentation and Compliance functions on release notes, validation evidence, and audit responses.
Qualifications
Experience of minumum 12 years of testing healthcare, EHR, or other regulated (HIPAA / SOC 2 / FDA-adjacent) software.
Direct experience evaluating or deploying AI-assisted testing tools or building test strategy for AI/LLM-powered product features.
Familiarity with Java/Spring-based enterprise applications and SQL Server-backed systems.
ISTQB Advanced Level or equivalent certification.
Representative Tooling Landscape
Automation: Selenium, Playwright, Cypress, REST Assured, Appium (mobile)
Performance: JMeter, k6, Gatling, NewRelic
Security: OWASP ZAP, Burp Suite, dependency/vulnerability scanners
AI-assisted testing: LLM-based test-case generation, self-healing automation, model-output evaluation frameworks
CI/CD & Test Management: Jenkins/GitHub Actions, Jira, TestRail or Qase
