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Ai Tester Jobs in Quebec (NOW HIRING)

PCL Construction

AI Analyst

Montreal, QC · On-site

Supporting user acceptance testing (UAT) and monitoring usage and performance of AI-enabled tools to identify improvement opportunities. * Helping build user confidence and capability with AI-enabled ...

PCL Construction

AI Analyst

Montreal, QC · On-site

Supporting user acceptance testing (UAT) and monitoring usage and performance of AI-enabled tools to identify improvement opportunities. * Helping build user confidence and capability with AI-enabled ...

PCL Construction

AI Analyst

Montreal, QC · On-site

Supporting user acceptance testing (UAT) and monitoring usage and performance of AI-enabled tools to identify improvement opportunities. * Helping build user confidence and capability with AI-enabled ...

Broccolini

Senior AI Skill builder

Kirkland, QC · On-site

The Senior AI Skill Builder will help accelerate deployment by building, testing, and refining reusable AI skills that support teams across construction, finance, HR, estimation, project management ...

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All JobsAi Tester JobsAi Tester Jobs in Quebec

Ai Tester information

See Quebec salary details

$10

$49

$94

How much do ai tester jobs pay per hour?

As of Aug 7, 2026, the average hourly pay for ai tester in Quebec is $49.44, according to ZipRecruiter salary data. Most workers in this role earn between $25.00 and $67.79 per hour, depending on experience, location, and employer.

Can I get an AI Tester job with no experience?

Entry-level AI Tester positions often do not require prior experience, but having basic knowledge of programming, testing tools, and AI concepts can improve your chances. Many employers offer training or consider candidates with relevant skills from related fields such as software testing or data analysis.

What are the key skills and qualifications needed to thrive as an AI tester?

To thrive as an AI Tester, you need a background in computer science, experience with software testing methodologies, and a solid understanding of artificial intelligence technologies. Familiarity with testing tools (such as Selenium, Jupyter notebooks, or TensorFlow testing frameworks), programming languages like Python, and relevant certifications (e.g., ISTQB) are highly advantageous. Attention to detail, problem-solving abilities, and strong communication skills help AI Testers identify and articulate issues effectively. These skills ensure AI systems are reliable, accurate, and deliver expected outcomes in real-world applications.

What is an AI tester?

An AI Tester is responsible for evaluating artificial intelligence systems to ensure they function correctly, efficiently, and ethically. They design and execute test cases, identify flaws or biases, and verify that AI models meet performance standards. AI Testers work with developers and data scientists to improve AI reliability and user experience. Their role is crucial in preventing errors, reducing risks, and ensuring AI models make accurate and fair decisions.

How do I become an AI tester?

To become an AI tester, you should have a strong understanding of software testing principles, programming skills in languages like Python or Java, and familiarity with AI and machine learning concepts. Gaining experience with testing tools, data annotation, and quality assurance processes can also be beneficial. Certifications in software testing or AI-related fields can enhance your qualifications.

How much do AI testers make?

AI testers typically earn between $50,000 and $100,000 annually, depending on experience, location, and the complexity of the projects. Entry-level positions may start lower, while experienced testers with skills in automation tools and programming can earn higher salaries.

What are some typical challenges faced by AI testers in their daily work?

AI Testers often encounter challenges such as managing and testing large, complex datasets, handling rapidly evolving algorithms, and ensuring consistent test coverage across various real-world scenarios. They must also validate that AI models are free from bias and produce accurate, reproducible results under different conditions. Overcoming these challenges requires both technical proficiency and adaptability. Collaboration with data scientists, developers, and product managers is common, and testers frequently update their testing approaches to keep pace with the fast changes in AI technology.

What are popular job titles related to Ai Tester jobs in Quebec? For Ai Tester jobs in Quebec, the most frequently searched job titles are:
What job categories do people searching Ai Tester jobs in Quebec look for? The top searched job categories for Ai Tester jobs in Quebec are:
Ai Tester jobs near you
Infographic showing various Ai Tester job openings in Quebec as of August 2026, with employment types broken down into 85% Full Time, 10% Part Time, and 5% Contract. Highlights an 85% In-person, 5% Hybrid, and 10% Remote job distribution, with an average salary of $102,827 per year, or $49.4 per hour.

QA Lead - AI Systems & Models Testing

Jay Analytix

Montreal, QC

Contractor

Re-posted 27 days ago

Job description

QA Lead - AI Systems & Models Testing

Quality Assurance Artificial Intelligence Contract Position

Contract

Montreal, QC

AI / ML Testing

LLM / RAG / LangChain

ABOUT THE ROLE

We are seeking an experienced QA Lead with deep expertise in AI systems testing to join our team on a contract basis in Montreal, Quebec. This role sits at the intersection of quality engineering and artificial intelligence, requiring hands-on proficiency in LLM behavior analysis, RAG pipeline validation, and modern AI orchestration frameworks. You will own the end-to-end test strategy for complex AI products and help define quality standards in a rapidly evolving space.

MUST-HAVE SKILLS

  • Proven QA leadership experience designing and executing test strategies for AI/ML systems or LLM-powered applications.
  • Strong understanding of LLM internals: tokenization, embeddings, attention mechanisms, and inference behavior to anticipate and diagnose failure modes.
  • Hands-on experience with prompt engineering - constructing effective prompts, detecting hallucinations, and evaluating outputs across accuracy, tone, coherence, and bias dimensions.
  • Experience testing RAG pipelines and knowledge base integrations, including validation of data quality and retrieval accuracy as they impact model outputs.
  • Familiarity with vector database mechanics: similarity search thresholds, embedding drift, near-duplicate documents, and sparse vs. dense embeddings.
  • Practical experience with LangChain and/or LangGraph - able to read chain/graph construction code, identify failure points, and write test harnesses.
  • Ability to validate MCP (Model Context Protocol) integration points, including tool availability and error-handling scenarios.
  • Proficiency applying generative AI evaluation metrics and establishing quality thresholds appropriate for production AI systems.
  • Excellent written and verbal communication in English; bilingualism (English/French) is a plus for the Montreal market.

NICE-TO-HAVE SKILLS

  • Experience with bias detection and safety testing frameworks for AI systems.
  • Exposure to performance and scalability testing of vector databases under high load.
  • Familiarity with CI/CD pipelines for ML model deployment and automated regression testing.
  • Knowledge of responsible AI principles and AI governance frameworks.
  • Contributions to or experience with open-source AI testing or evaluation tooling (e.g., DeepEval, Ragas, PromptFlow).
  • Background in data engineering or data quality practices relevant to AI pipeline inputs.
  • Cloud platform experience (AWS, Azure, or GCP) in the context of deploying or testing AI workloads.

KEY RESPONSIBILITIES

  • Lead design and execution of comprehensive test strategies across AI systems, including prompt evaluation, output quality assessment, and bias/safety analysis.
  • Develop and maintain test harnesses for LangChain and LangGraph-based applications; review chain and graph construction code to proactively surface integration risks.
  • Validate RAG pipeline integrity - data ingestion, chunking, retrieval accuracy, and embedding consistency - and define edge-case coverage for vector database interactions.
  • Establish and track generative AI quality metrics and thresholds; report on model output quality across multiple evaluation dimensions.
  • Collaborate with ML engineers, data scientists, and product teams to embed quality practices throughout the AI development lifecycle.
  • Document test findings clearly for both technical and non-technical stakeholders.

Contract position based in Montreal, Quebec, Canada On-site / Hybrid

Employment Type: CONTRACTOR

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