AI-First Quality Engineering Lead

RBC•Toronto, ON

24d•Onsite

About The Position

As an AI-First Quality Engineer Lead in QEX & DevOps, you will be a driving force in fundamentally reimagining how RBC builds and delivers software — where AI is not a feature of the QE practice, but the foundation it is built on. You will partner across technology, product, and platform teams to lead a full shift-left transformation powered by GenAI, intelligent automation, and agentic engineering practices. This is not a role about managing test pipelines — it is about building a quality engineering organization that thinks, operates, and scales with AI at its core. In this role, you will leverage your full-stack engineering experience and AI fluency to go beyond automating the SDLC. You will embed intelligence directly into RBC's applications, tools, and delivery ecosystems — using LLMs, AI agents, and predictive analytics to detect risk earlier, accelerate delivery, and raise the bar on software quality at enterprise scale. We are looking for a bold, future-forward engineer who can sustain and extend RBC's leadership position in the industry — someone who sees AI not as a productivity add-on, but as a strategic capability to be engineered, governed, and continuously evolved. You will set the vision, build the culture, and model the AI-first mindset that will define the next era of quality engineering at RBC, all while upholding RBC's Values and guiding principles.

Requirements

Hands-on experience using AI/ML tools in a QE context — e.g., GitHub Copilot, LLM-based test generation, AI-driven test analysis platforms
Proficiency in Python, Java, or .NET with demonstrated use of AI/LLM APIs (Anthropic, OpenAI, etc.) to build intelligent automation
Strong understanding of agentic workflows and how to apply multi-step AI reasoning to complex testing problems
Experience building or evaluating AI-powered tools: self-healing selectors, visual AI testing, defect clustering, predictive analytics
Working knowledge of Git and modern DevOps toolchains with AI integration points (e.g., AI-assisted PR review, intelligent pipeline observability)
AI-first mindset — demonstrated track record of reaching for AI-based solutions first when designing QE strategy, not retrofitting AI onto existing processes
4 years in QE/test engineering with expertise across cloud, distributed systems, APIs, databases, and mainframe environments
Expert in defining EVALs, SKILLS, test and automation strategies with a focus on measurable outcomes — coverage, velocity, defect escape rate — powered by AI-driven insights
Experience with GenAI application testing considerations: hallucination detection, non-determinism, prompt regression, and responsible AI validation
Computer Engineering, Computer Science, related (technical) degree/diploma, or related breadth of experience

Nice To Haves

Prior working experience in financial industry

Responsibilities

Lead AI-augmented quality engineering — design and champion testing strategies where AI/ML tools are the default, not an afterthought, across the full software delivery lifecycle
Build AI-native automation frameworks — architect test ecosystems that leverage LLMs, generative AI, and intelligent agents to autonomously generate, execute, and triage test cases
Evaluate, pilot, and operationalize AI-powered QE tools (e.g., self-healing test suites, AI-based defect prediction, intelligent test selection) to replace manual and legacy processes
Embed AI into cloud-scale delivery pipelines — integrate AI-assisted quality gates into CI/CD workflows across distributed, cloud-native, and mainframe environments
Solve hard problems with AI — prototype and validate novel AI-driven approaches to test coverage gaps, flaky test detection, and production observability