Quality Engineer III (AI Automation)

Work Location:

Toronto, Ontario, Canada

Hours:

0

Line of Business:

Technology Solutions

Pay Details:

$126,800 - $164,100 CAD

This role is eligible for a discretionary variable compensation award that considers business and individual performance.

TD is committed to providing fair and equitable compensation opportunities to all colleagues. Growth opportunities and skill development are defining features of the colleague experience at TD. Our compensation policies and practices have been designed to allow colleagues to progress through the salary range over time as they progress in their role. The base pay actually offered may vary based upon the candidate's skills and experience, job-related knowledge, geographic location, and other specific business and organizational needs.

As a candidate, you are encouraged to ask compensation related questions and have an open dialogue with your recruiter who can provide you more specific details for this role.

Job Description:

Global Transaction Banking (GTB) is a key growth business within TDS that provides the opportunity to make an impact with top-tier organizations. We offer innovative solutions and treasury advisory services on large and complex liquidity, payments, and trade finance needs. The business constantly changes with macroeconomic conditions, unprecedented levels of innovation, interest rate environments, and foreign exchange movements. As a result, all GTB business lines continue to evolve to provide optimum trade finance, liquidity, and payment solutions to clients.

Global Transaction Banking technology team focuses on delivering top tier technology solutions to enable and grow the global GTB business.

Summary

This role is a senior, hands‑on Quality Engineering position with a strong focus on automation engineering, framework development, and AI‑driven QE innovation. The role owns end‑to‑end automation delivery within an agile pod on complex initiatives, while also bringing new ideas and capabilities to evolve the QE practice through AI‑assisted testing and tooling.

The QE Engineer is accountable not only for what is tested, but how testing is engineered—designing reusable automation components, building internal QE tools, improving signal quality, and applying AI techniques to increase coverage, speed, and effectiveness of quality validation..

Core QE & Automation Responsibilities

• Own hands-on automation delivery for a pod: define automation scope, API/UI/integration coverage, test architecture, and entry/exit criteria for sprint and release readiness.

• Design, build, and maintain scalable, reusable automation framework components (API, UI, data, contract testing) aligned with enterprise QE standards.

• Translate functional requirements, NFRs, and architecture into risk-based, automation-first test strategies with clear and measurable outcomes.

• Partner with the E2E Lead to contribute pod-level scenarios, automation assets, and execution support for integrated E2E test cycles and major releases.

• Perform deep defect triage and root-cause analysis using logs, traces, metrics, and test telemetry, drive prevention through targeted automation and improved test design.

AI-Driven QE Innovation

• Actively introduce and experiment with AI usage in QE, with a focus on practical adoption—not research.

• Leverage Copilot and LLM-assisted tools to:

• o Accelerate test case creation, refactoring, and coverage expansion

• o Improve readability, maintainability, and consistency of automation code

• o Assist with exploratory testing, scenario generation, and edge-case discovery

• Build or extend AI-assisted QE utilities and tools, such as:

• o Intelligent test data generation and masking tools

• o Automated analysis of test failures, flakiness, and defect patterns

• o LLM-assisted reporting and summarization for QE and release readiness

• Apply agent-based or prompt-driven approaches (e.g., LangChain-style orchestration) where appropriate for QE tasks, aligned with AI enablement initiatives.

• Continuously identify QE workflows that can be augmented or automated with AI, and prototype solutions that can be scaled across teams.

Engineering & Practice Contribution

• Build internal QE tooling for reporting, dashboards, and automation health (coverage, flakiness, defect leakage, signal quality).

• Integrate automated tests and QE tools into CI/CD pipelines, enabling fast, reliable feedback with minimal manual intervention.

• Champion modern QE practices including contract testing, service virtualization, data-driven testing, and CI/CD quality gates.

• Partner with the Automation Practice Lead to evolve QE standards, frameworks, and governance, contributing reusable assets back to the practice.

• Mentor junior and mid-level QE engineers; raise the overall engineering bar for QE within the pod.

• Drive quality ownership in agile ceremonies and influence engineering teams to build testability and quality into design.

Education &