AI-First Quality Engineering Lead

What is the opportunity?

As 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.

What will you do?

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

What do you need to succeed?

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)