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Ai Tester Jobs in Quebec (NOW HIRING)

AI Enablement Specialist Build the future with us Are you driven by AI and eager to contribute to ... Run user acceptance testing before rollout, and adjust solutions based on what testing shows ...

AI Analyst

Montreal, QC ยท 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 ...

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 ...

Implement development best practices, including testing, CI/CD, observability, monitoring, and ... Contribute to AI standards, reusable components, and development practices that support CIMA ...

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 ...

Contribute to model versioning, testing, monitoring, and operationalization activities. * Assist in troubleshooting production AI workloads and performance issues. Governance, Security and ...

CA$120K - CA$140K/yr

This role combines hands-on manual testing, test automation development, and the use of AI tools in the SDLC. You will contribute to building reliable test coverage, improving automation, and ...

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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Ai Tester information

See Quebec salary details

$10

$49

$94

How much do ai tester jobs pay per hour?

As of Sep 1, 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.

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.

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 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.

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 are willing to hire candidates with relevant skills or certifications, such as in software testing or machine learning fundamentals.

How do you become an AI tester?

To become an AI tester, you typically need a background in computer science, software testing, or related fields, along with knowledge of machine learning and AI concepts. Gaining experience with programming languages like Python and familiarity with testing tools and frameworks are also important. Certifications in software testing or AI can enhance your qualifications and job prospects.

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 job categories do people searching Ai Tester jobs in Quebec look for?

The top searched job categories for Ai Tester jobs in Quebec are:

Infographic showing various Ai Tester job openings in Quebec as of August 2026, with employment types broken down into 75% Full Time, 10% Part Time, 5% Temporary, and 10% Contract. Highlights an 90% In-person, 5% Hybrid, and 5% Remote job distribution, with an average salary of $102,827 per year, or $49.4 per hour.

QA Lead - AI Systems & Models Testing

Montreal, QC โ€ข On-site

Contractor

Re-posted 22 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