Entity: WELLSTARPosition Title: Senior Quality Engineering Lead, AI-Driven TestingSalary Range: $125,000 - $155,000Job Class: Full TimeWork Location: Toronto, ON About the Team:The WELLSTAR Clinical Platform Group is responsible for the development and support of the EMR platforms such as OSCAR and Cerebrum, which are leading Electronic Medical Record (EMR) software's in Canada that supports thousands of providers and millions of Canadian patients across the country.
Primarily located in the greater Toronto area and the greater Vancouver area, the team currently works remotely with hybrid support where desired.
Position Summary:We are looking for a senior, hands-on Quality Engineering Lead to help modernize how WELLSTAR's EMR teams approach testing, release confidence, and quality in an AI assisted engineering environment.
As AI-assisted development increases the speed and volume of software change, this role will help define how quality engineering, automation, AI-assisted test generation, and risk based release practices evolve to keep pace without compromising reliability, privacy, or patient safety.
This is not a traditional QA lead role. You will not simply manage test execution or apply a standard QA playbook. Instead, you will assess existing QA practices, align teams around practical quality standards, evaluate emerging AI-driven testing approaches, and help define what "safe to release" means for critical healthcare workflows.
The right person does not need to have all the answers. This is a genuinely emerging field. We are looking for someone curious, experimental, pragmatic, and technically credible; someone who wants to learn deeply, test new approaches, separate hype from real value, and help define the future of quality in an AI-assisted engineering environment.
The Problem You'll Help Us Solve:Our teams are using AI-assisted development tools to build and modify software faster. This creates a new quality challenge: code volume and change velocity are increasing faster than traditional QA, regression testing, and test maintenance can comfortably handle.
At the same time, QA and automation practices are not yet consistent across EMR groups. Some practices work well, others need to be improved, and some may need to be rethought from first principles.
We already have a foundation of AI-assisted automated testing in place. We have also begun developing AI-driven approaches to manual QA, using user-driven requirements and acceptance criteria as the basis for test case generation. The successful candidate will assess, extend, refine, and operationalize this work at scale rather than starting from zero.
This role is about shaping a modern quality practice, not simply applying a classic QA playbook.
What you will be doing: You will co-own quality with Engineering and Product. Engineering remains accountable for code quality, unit tests, integration tests, CI/CD implementation, and technical gates. This role owns QA strategy, cross-team quality practice alignment, test approach, release readiness evidence, and the effective use of AI and automation to improve testing confidence.
This includes regression, smoke, spot, and end-to-end testing approaches; maintainable QA automation direction; AI-assisted test generation from requirements and acceptance criteria; AI-enabled improvements to manual-style QA processes; release-readiness evidence from a QA perspective; and consistent cross-team quality standards and practices.
You will not have direct QA reports, but you will influence QA contributors, engineers, product managers, and engineering leaders through credibility, clarity, and practical guidance.
QA Alignment & Standardization: Align QA practices across EMR groups where current approaches are inconsistent or suboptimal. Define practical quality standards for requirements, acceptance criteria, test coverage, regression risk, and release readiness; creating a coherent, modern baseline across teams while respecting product-specific needs.
AI-Driven Test Generation:
Develop and operationalize AI-assisted approaches for generating test cases from requirements and acceptance criteria. Explore how AI can support QA through scenario discovery, edge-case identification, exploratory testing prompts, and regression-risk analysis; reducing the manual effort required to maintain coverage as the codebase grows. You will evaluate where AI meaningfully improves quality practices, where it does not, and how to adopt it responsibly in a healthcare environment where privacy, reliability, and patient safety matter.
Coverage Intelligence: Use AI and modern quality practices to continuously identify and close testing coverage gaps that would otherwise go undetected. Evaluate existing automation and determine where to extend, improve, replace, or rebuild based on long-term maintainability and effectiveness. Guide automated smoke, spot, regression, end-to-end, API, UI, and workflow testing strategies across critical EMR capabilities.
Regression & Release Confidence: Help define what "safe to release" means from a QA perspective and what evidence teams should have before release. Improve release-readiness practices for critical clinical workflows across OSCAR, Cerebrum, and related capabilities. This includes regression coverage, smoke testing, workflow validation, risk-based testing, test data considerations, and QA evidence that helps Engineering, Product, and leadership make informed release decisions.
Proof of Concept & Tooling Evaluation: Run proofs of concept and tooling evaluations to determine how AI, automation, and modern testing practices can meaningfully improve quality practices. Communicate quality risks clearly to engineering, product, and leadership. Champion a culture where quality is shared across QA, Engineering, and Product rather than treated as a downstream checkpoint.
Healthcare Interoperability: Support healthcare interoperability testing involving HL7, FHIR, APIs, and healthcare data exchange workflows. Help validate critical clinical workflows; including patient records, scheduling, billing, and integrations meet correctness and reliability standards.
Engineering Collaboration: Partner with engineering to support better unit and integration testing practices, while recognizing that engineers own the tests for the features they build. Support Engineering on unit and integration testing standards and in shaping CI/CD quality expectations, while Engineering continues to own the pipelines and gates.
You are: - Curious. You are energized by emerging fields. You experiment, run PoCs, and learn as much from what doesn't work as from what does.
- Pragmatic. You separate hype from value and build approaches that are practical, maintainable, and actually adopted by teams.
- Influential. You bring engineers, QA contributors, and product managers along through credibility and clear communication - not authority.
- Technical. You understand development workflows deeply enough to collaborate with engineers as a peer, grasp implementation tradeoffs, and guide automation decisions.
- Adaptable. You are comfortable with ambiguity and can shift direction as you learn what works in a genuinely emerging field.
- Communicator. You explain quality risks, tradeoffs, and recommendations clearly to both technical and non-technical audiences.
- A strong technical or development background is a significant asset, but deep programming ability is not the primary requirement.
- Fluency with AI tools and a curiosity driven approach to leveraging them for quality engineering will matter more than raw coding skill. The ideal candidate should understand development workflows deeply enough to collaborate credibly with engineers, grasp implementation tradeoffs, and guide practical automation decisions.
- Quality engineering strategy across regression, smoke, spot, end-to-end, API, UI, workflow testing, release readiness, risk-based testing, test coverage, and test data management.
- Test automation experience with tools such as Robot Framework, Playwright, Cypress, Selenium, or similar.
- API and integration testing experience with tools such as Postman/Newman, REST Assured, or similar.
- Technical fluency with test frameworks such as JUnit, TestNG, or comparable frameworks, and programming or scripting languages such as Java, C#, Kotlin, TypeScript, JavaScript, Python, or similar.
- Experience with, or strong curiosity about, AI-enabled QA tools for test generation, test maintenance, requirements analysis, coverage analysis, defect detection, and regression planning.
- Exposure to healthcare interoperability standards and workflows such as HL7, FHIR, healthcare APIs, provincial health integrations, or healthcare data exchange.
- Experience in EMRs, EHRs, digital health, clinical systems, Canadian healthcare, primary care workflows, or other regulated, privacy-sensitive, safety-critical software environments.
- Experience helping teams modernize or align quality practices across multiple products, platforms, or engineering groups.
The salary for this position falls within a defined range and will be determined based on several factors, including the candidate's experience, qualifications, skills, and the needs of the organization. At WELL, we are committed to fair and equitable compensation and aim to provide a competitive salary that reflects the value and expertise of the successful candidate.
