RQ09644 - Sr. Systems Testing/QA Specialist

Source Code; Toronto; Hybrid; CAD 110,000 - 150,000; Full time

Job summary

Source Code is seeking a Sr. Systems Testing/QA Specialist in Downtown Toronto with a hybrid work arrangement (3 days onsite, 2 days remote).

You will lead testing of web and mobile applications, validate AI/ML-enabled features, and ensure biometric integrity within an Agile/DevOps environment. The ideal candidate has 10+ years of QA experience, proficiency in automated tools (Selenium, Cypress, Playwright), and a solid grasp of accessibility standards (AODA/WCAG).

Qualifications

• 10+ years of QA experience in software testing.
• Experience testing web and mobile applications.
• Proficient in automated testing with Selenium, Cypress, or Playwright.
• Experience testing AI/ML-enabled applications and AI explainability.
• Familiar with AI transparency principles and biometric integrity checks.
• Strong Agile/DevOps collaboration within cross-functional teams.
• Deep understanding of accessibility standards (AODA/WCAG 2.1).
• Knowledge of AI model behavior, bias detection, and related tooling.

Skills

Web testing

QA experience

Agile / DevOps

AI testing

AI explainability

Biometric testing

Accessibility (AODA/WCAG)

Automation mindset

Tools

Selenium

Cypress

Playwright

MLFlow

TensorBoard

Job description

RQ09644 - Sr. Systems Testing/QA Specialist

About the job RQ09644 - Sr. Systems Testing/QA Specialist

RQ09644 - Sr. Systems Testing/QA Specialist

Downtown, Toronto

Hybrid: 3 Days onsite / 2 days remote

Must Haves:

• Experience in testingweb/mobile applications
• 10+ years of experience as a QA.
• Proficiency in automated testing tools (e.g., Selenium, Cypress, Playwright).
• Experience testing AI/ML-enabled applications**
• Familiarity with AI explainability as well as AI transparency principles.*
• Ability to validate AI-assisted user workflows and ensure biometric integrity.*
• Experience working within an Agile environment - DevOps
• applying deep understanding of AODA / WCAG 2.1 success criteria (Levels A, AA, AAA), including principles, success criteria and techniques.

Nice to have:

• Understanding / Experience with AI testing tools and frameworks (e.g., MLFlow, TensorBoard).
• Understanding of AI model behaviour, including edge cases and bias detection.