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Time Type:

Job Description :

Specialist, QA Test Automation

POSITION SUMMARY

We aren't looking for an all-knowing AI guru to define our entire organizational strategy from scratch. We are looking for a sharp, curious Specialist, QA Test Automation who can bridge the gap between strategic vision and day-to-day technical execution. Working closely with the Chapter Lead, QA — who provides the overarching AI QA strategy—you will be embedded within Cogeco’s AI squads to bring that strategy to life.

In this role, you will move past standard "pass/fail" scripts to tackle the complex world of non-deterministic outputs. You will focus heavily on probabilistic testing, curating golden datasets, and leveraging cutting-edge LLM evaluation tools like Promptfoo. If you have a solid foundation in QA or software engineering, a healthy obsession with automation, and a restless drive to learn, you’ll fit right in.

KEY RESPONSIBILITIES

Probabilistic Testing & Evaluation

• Embrace the Unpredictable: Implement and refine testing workflows tailored for AI powered solutions where outputs are probabilistic rather than deterministic.
• Regression & Drift Detection: Build guardrails to identify model drift, prompt regression, hallucination risks, and safety violations before they hit production.

Dataset Curation & Tooling

• Golden Datasets: Own the creation, maintenance, and scaling of high-quality golden datasets used as the source of truth for benchmarking model changes.
• AI Eval Tooling: Champion programmatic evaluation tools such as Promptfoo, DeepEval, or Ragas to automate the assessment of prompt variants and model performance.

Strategy Execution & Collaboration

• Chapter Alignment: Partner closely with the Chapter Lead, QA to translate high-level AI QA frameworks into actionable, daily tasks for technical teams.
• Feedback Loop: Provide real-time feedback to the Chapter Lead on how the strategy is performing in the trenches, recommending practical pivots based on real-world results.

Automation & CI/CD Integration

• Smart Pipelines: Collaborate with DevOps specialists to inject AI evaluation metrics directly into modern CI/CD pipelines, ensuring automated gates handle fluid AI responses.
• Data Validation: Validate data pipelines feeding our models, ensuring that data integrity is maintained from ingestion to inference.

ESSENTIAL QUALIFICATIONS

Academic Training & Mindset

• College diploma or Degree in Computer Science, Engineering, or a related discipline (or equivalent practical experience).
• The Right Mindset: A strong foundation in core QA principles combined with a passion for AI/ML. You don't need a PhD; you just need to be eager to learn how LLMs behave.

Work Experience & Technical Skills

• 3+ years of experience in Quality Assurance, Software Engineering, or Data Engineering.
• Hands-on Scripting: Experience in Python or JavaScript/TypeScript (essential for configuring evaluation frameworks like Promptfoo and manipulating test data).
• Modern QA Ecosystem: Experience with test automation frameworks, API testing, and code repositories (Git).

STRONG ASSETS

• Exposure to Golden Datasets: creating, maintenance, and/or scaling of high-quality golden datasets used as the source of truth for benchmarking model changes.
• Expsosure to AI Eval Tooling: Champion programmatic evaluation tools such as Promptfoo, DeepEval, or Ragas to automate the assessment of prompt variants and model performance.

• CI/CD Familiarity: Experience working with pipeline tools (e.g., Bitbucket pipelines, Google Cloud run, GitHub Actions, GitLab CI, Jenkins).

SPECIFIC COMPETENCIES

Technical Execution & Curiosity

• Analytical Deep-Dives: Comfortable analyzing data inputs/outputs and debugging prompts to identify the root causes of unexpected AI behavior.
• Tool Adaptability: Ready to quickly learn, test, and implement new open-source AI evaluation and observability tooling as the ecosystem evolves.

Growth Mindset &