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Ai Test Engineer Jobs in Quebec (NOW HIRING)

Apertera

Generative AI Engineer

Montreal, QC · On-site

Generative AI EngineerAbout Apertera Apertera is leading the evolution of language solutions for ... Work closely with machine learning engineers and data engineers to design, build, and test models.

Artefact

AI & Agentic Engineer, Senior

Montreal, QC · On-site

Write evaluation suites and regression tests for LLM-powered features, and monitor cost, latency ... Work AI-Natively and Client-Facing Our engineers work AI-natively and represent Artefact directly ...

TTC Global

Senior Test Automation Engineer

Montreal, QC · On-site +1

... AI solutions or LLM based systems Experience working in complex enterprise environments or large ... test automation and quality engineering in senior or lead roles Demonstrated ability to drive ...

Quadbridge

AI Application Developer

Montreal, QC · On-site

Develop, test, and deploy functional automation and AI solutions (e.g., Generative AI, semi ... Azure AI Engineer Associate, Microsoft Certified: Power Platform Solution Architect Expert) Why ...

Quadbridge

AI Application Developer

Montreal, QC

Develop, test, and deploy functional automation and AI solutions (e.g., Generative AI, semi ... Azure AI Engineer Associate, Microsoft Certified: Power Platform Solution Architect Expert) Why ...

SAP

SAP iXp Intern - AI Engineer

Montreal, QC · On-site

SAP iXp Intern - AI Engineer Location: Montreal, QC Expected start date to end date: September 2026 ... automated tests and evaluation metrics for model quality, hallucinations, latency, and cost.

Sonepar

AI Specialist

Laval, QC · On-site

Design, build, test, and deploy AI solutions including Copilot Studio agents, Microsoft Foundry ... Collaborate with data engineering, data science, architecture, security, and platform teams to ...

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All JobsAi Test Engineer JobsAi Test Engineer Jobs in Quebec

Ai Test Engineer information

What is an AI test engineer?

AI Test Engineers are professionals who design, develop, and execute tests to evaluate the performance, accuracy, and reliability of artificial intelligence systems and machine learning models. They work closely with data scientists and software developers to ensure AI solutions function as intended, identifying bugs, biases, and potential risks in algorithms. Their responsibilities often include creating test cases, automating test processes, and analyzing results to improve AI system quality and compliance.

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

To thrive as an AI Test Engineer, you need a strong background in computer science, software testing methodologies, and a good understanding of machine learning concepts, often supported by a relevant degree. Familiarity with programming languages like Python, testing frameworks such as pytest, and tools like TensorFlow or PyTorch, along with certifications in software testing or AI, are typically required. Analytical thinking, attention to detail, and effective communication set top performers apart in this role. These skills and qualities are crucial for ensuring the reliability, accuracy, and ethical deployment of AI systems.

What are some common challenges AI test engineers face when validating machine learning models?

AI Test Engineers often encounter challenges such as ensuring the quality and fairness of machine learning models, identifying edge cases that the model may not handle well, and working with limited labeled data for testing. Additionally, interpreting test results can be complex due to the probabilistic nature of AI outputs, requiring close collaboration with data scientists to understand model behaviors. Effective communication and a strong foundation in both software testing and AI concepts are essential to address these challenges and ensure reliable AI solutions.

What is the difference between Ai Test Engineer vs Data Scientist?

AspectAi Test EngineerData Scientist
Required CredentialsBachelor's in CS, AI, or related field; knowledge of testing toolsBachelor's or higher in CS, Statistics, or related; proficiency in data analysis
Work EnvironmentSoftware testing teams, AI development projectsData analysis teams, AI research projects
Employer & Industry UsageTech companies, AI startups, software firmsTech companies, finance, healthcare, research institutions
Common Search & ComparisonOften compared for roles in AI testing and quality assuranceCompared for data analysis and AI model development roles

While both roles work within AI and tech environments, Ai Test Engineers focus on testing and validating AI systems, ensuring quality and performance. Data Scientists analyze data to develop models and insights. The roles are complementary but distinct in their core responsibilities.

How do I become an AI Test Engineer?

To become an AI Test Engineer, candidates typically need a strong background in computer science, software testing, or related fields, along with knowledge of AI and machine learning concepts. Skills in programming languages such as Python or Java, experience with testing tools, and understanding of AI model evaluation are essential. Earning relevant certifications and gaining experience in software development and testing environments can also improve job prospects.

What are popular job titles related to Ai Test Engineer jobs in Quebec?

For Ai Test Engineer jobs in Quebec, the most frequently searched job titles are:

What job categories do people searching Ai Test Engineer jobs in Quebec look for?

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

Ai Test Engineer jobs near you
Infographic showing various Ai Test Engineer job openings in Quebec as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 10% Part Time, 3% Contract, and 1% Nights. Highlights an 90% Physical, 2% Hybrid, and 8% Remote job distribution.

AI Solutions Engineer

Connor, Clark & Lunn Financial Group Ltd.

Montreal, QC • Hybrid

Full-time

Re-posted 11 days ago

Job description

Interested in joining one of Canada's top-performing asset managers? We're hiring an AI Solution Engineer in our AI Solutions engineering team. You build the AI systems that Connor, Clark & Lunn Financial Group and our affiliate teams use in day-to-day work.  You turn signed-off specifications into production-ready AI assistants, agents, and workflow automations. You own build quality, reliability, safety, traceability, and maintainability, and partner closely with Data Engineering and MLOps to ship responsibly in a regulated financial services environment. We operate on a hybrid model with three days a week in-office to facilitate team collaboration.   

 
What You Will Do 

  • Build AI assistants and agents end-to-end from a signed-off spec, retrieval, tool integrations, prompt logic, source citation, and workflow integration 
  • Design and maintain retrieval pipelines, chunking strategy, metadata schema, indexing, access controls, and query optimization 
  • Engineer prompts with discipline, write, test, evaluate, and iterate; document failure modes and edge cases 
  • Own code quality and handoff, version artifacts, write tests where appropriate, and maintain clean, reviewable documentation 
  • Partner with Data Engineering to make data retrieval-ready, define ingestion needs, document assumptions, and validate data quality impacts 
  • Deploy through standard MLOps pipelines, monitoring/alerting, rollback readiness, cost controls, and operational runbooks 
  • Collaborate with affiliate teams during builds, demo real increments, capture feedback, and incorporate changes without breaking scope 
  • Document known limitations, risks, and mitigations before UAT, set expectations and prevent surprises for business stakeholders 

What You Will Bring 

  • Strong Python skills with experience shipping LLM applications end-to-end (build, test, deploy, and operate) 
  • Hands-on RAG experience, document processing, vector databases/search, and retrieval evaluation (precision/recall, grounding quality) 
  • Experience with agent frameworks (e.g., LangChain, LlamaIndex or equivalents), including tool use, orchestration, and multi-step flows 
  • Experience on enterprise AI platforms (e.g., Azure OpenAI, Google Vertex AI, Anthropic APIs), including security and cost/performance trade-offs 
  • Prompt engineering fundamentals, structured prompting, output constraints, adversarial/failure-mode testing, and reproducibility 
  • Comfort working with semi-structured/unstructured data (PDFs, financial docs, emails, notes) and translating it into retrieval-ready assets 
  • Delivery mindset and strong written communication, hold scope, write clear technical documentation, and finish to production-quality 

#LI-Hybrid #LI-KC1 

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