AI Testing
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AI Summary
BCE Global Tech's Global Quality Engineering (GQE) function is
building one of Canada's most ambitious AI quality programs — certifying every
AI and agentic system deployed across Bell Canada before it reaches production.
As a QA AI Specialist, you sit at the intersection of artificial intelligence,
software engineering, and quality assurance: a hybrid role that does not yet
have a textbook, because the discipline is being written in real time.
You will do two things simultaneously. First, you will bring
AI into GQE's existing testing practice — embedding AI-powered capabilities
into the test automation tooling, pipelines, and frameworks that 250 QA
engineers already use every day. Second, you will build and operate the
evaluation frameworks that test the AI systems being created by other Bell
engineering teams — agents, orchestration pipelines, RAG applications,
Salesforce AgentForce workflows, and ServiceNow Now Assist integrations.
Requirements
1. AI-Enhanced QA Tooling
Modernize GQE’s QA stack by embedding AI to improve speed, coverage, and intelligence:
- Integrate AI-driven test generation into Selenium, Playwright, and Postman frameworks
- Use predictive models to prioritize tests based on code changes and defect history
- Enable self-healing automation for UI/API changes
- Automate defect triage and root-cause analysis using failure clustering
- Support natural-language test authoring (English/French) for non-technical QA
- Continuously pilot emerging AI testing tools via a technology radar
2. AI Evaluation & Quality Pipelines
Build scalable evaluation systems tailored for AI behavior, not rule-based logic:
- Implement LLM-as-Judge pipelines on Vertex AI (Gemini) across key quality dimensions.
- Generate large, diverse, and adversarial test corpora from seed intents
- Evaluate RAG systems using metrics like faithfulness, relevance, and recall (RAGAS)
- Validate multi-step agent workflows, tool usage, and escalation behavior
- Embed AI evaluations into CI/CD as mandatory release gates.
3. AI Safety & Adversarial Testing
Operate a dedicated AI red-teaming capability to uncover AI-specific risks:
- Execute prompt injection and poisoned-context attacks on RAG systems.
- Run automated jailbreak and constraint-bypass probes (e.g., Garak)
- Systematically test hallucination, numerical accuracy, and domain knowledge
- Assess toxicity, bias, and fairness across English and French interactions
- Stress-test agentic systems for runaway actions and scope violations
4. Continuous Quality Evolution
Ensure the quality framework evolves as models and systems change:
- Monitor production AI outputs for quality drift and trigger re-certification
- Feed real production failures back into the test corpus
- Track model/version changes and generate quality delta reports.
- Maintain a living benchmark of Bell-specific AI quality standards
- Continuously adopt new evaluation research and industry best practices
- Partner early with AI/ML teams to embed quality by design
5. AI Quality Certification Operations
Lead technical execution of the AIQC program:
- Own Tier 2 & 3 certification testing from corpus design to red-teaming
- Calibrate LLM-as-Judge rubrics using human-labeled golden datasets
- Produce clear AI Quality Certificates with scores, risks, and conditions
- Advise teams on AI testability, prompts, and evaluation instrumentation
- Contribute to AIQC playbooks, documentation, and knowledge sharing
Required
▸ 5+ years of software quality engineering
experience, with at least 2 years working directly with AI/ML systems, LLMs, or
AI-powered applications
▸ Hands-on experience building or evaluating
LLM-based applications — including prompt engineering, RAG pipelines, or
agentic workflows
▸ Proficiency in Python: test framework
development, API integration, data processing, and evaluation scripting
▸ Experience with modern test automation
frameworks (Playwright, Selenium, Pytest, RestAssured, Postman/Newman) and
CI/CD platforms (GitHub Actions, Google Cloud Build, Jenkins)
▸ Working knowledge of at least one major AI/ML
platform — Google Vertex AI, Azure OpenAI, or AWS Bedrock — with hands-on API
usage
▸ Strong conceptual understanding of how LLMs
work: tokenization, temperature and sampling, context windows, grounding,
hallucination mechanics, and fine-tuning
▸ Demonstrated ability to design test strategies
for non-deterministic systems — moving beyond assertion-based testing to
probabilistic, rubric-based evaluation
Benefits
What We Offer:
- Competitive salaries and comprehensive health benefits
- Flexible work hours and remote work options
- Professional development and training opportunities
- A supportive and inclusive work environment
- Access to cutting-edge technology and tools.
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