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Explainable Ai Jobs in Quebec (NOW HIRING)

Jay Analytix

AI Engineer - Banking Domain

Montreal, QC · On-site

Knowledge of OSFI E-23 model governance, PIPEDA, and explainable AI for audits. * Experience with SQL and distributed data platforms (Spark, Databricks, or Snowflake). GOOD TO HAVE * Azure AI-102 ...

Maya HTT

AI Presales Engineer

Montreal, QC

Support opportunities involving RapidMiner, M-Bot, advanced analytics, explainable AI, AI automation, and enterprise AI tools * Collaborate with sales teams on proposals, scopes of work, solution ...

Maya HTT

AI Presales Engineer

Montreal, QC

Support opportunities involving RapidMiner, M-Bot, advanced analytics, explainable AI, AI automation, and enterprise AI tools * Collaborate with sales teams on proposals, scopes of work, solution ...

All JobsExplainable Ai JobsExplainable Ai Jobs in Quebec

Explainable Ai information

What is the difference between Explainable Ai vs Data Scientist?

AspectExplainable AiData Scientist
CredentialsTypically requires knowledge of AI, machine learning, and data analysis; certifications like AI or ML courses are commonRequires degrees in computer science, statistics, or related fields; certifications in data analysis or machine learning are beneficial
Work EnvironmentWorks within AI development teams, focusing on model transparency and interpretabilityWorks across data analysis, model building, and business insights, often in research or corporate settings
Industry UsageUsed in AI development, healthcare, finance, and any field requiring transparent AI modelsApplied in tech, finance, healthcare, and research for data-driven decision making

Explainable Ai focuses on making AI models transparent and understandable, ensuring trust and compliance. Data Scientists develop and analyze models, often working with complex data. While both roles involve AI and data, Explainable Ai specialists emphasize interpretability, whereas Data Scientists focus on model creation and insights.

What are some of the typical challenges faced when working in Explainable AI and how do professionals address them?

Professionals in Explainable AI often encounter challenges such as balancing model accuracy with interpretability, translating complex model outputs into understandable insights for non-technical stakeholders, and ensuring transparency without compromising sensitive data. Addressing these issues typically involves using specialized tools and frameworks for visualization, collaborating closely with data scientists, domain experts, and business teams, and staying updated on the latest research in model interpretability. Continuous learning and open communication are key to overcoming these challenges and delivering AI solutions that are both effective and trustworthy.

What are the key skills and qualifications needed to thrive as an Explainable AI specialist, and why are they important?

To thrive as an Explainable AI specialist, you need a strong background in machine learning, data science, and statistics, typically with an advanced degree in computer science or a related field. Familiarity with frameworks such as TensorFlow, PyTorch, and libraries like LIME or SHAP, as well as experience in model interpretability tools, is essential. Strong analytical thinking, effective communication, and the ability to translate complex technical concepts for non-technical stakeholders are crucial soft skills. These capabilities ensure that AI models are transparent, trustworthy, and can be responsibly integrated into decision-making processes.

What is Explainable AI?

Explainable AI (XAI) refers to methods and techniques in artificial intelligence that make the results of AI models understandable and interpretable by humans. XAI aims to provide transparency into how AI systems make decisions, helping users trust and effectively manage AI applications. This is especially important in fields like healthcare, finance, and law, where understanding the reasoning behind AI-driven outcomes can be crucial for accountability and compliance. By making AI more transparent, XAI also helps identify and address biases or errors in AI systems.
What are popular job titles related to Explainable Ai jobs in Quebec? For Explainable Ai jobs in Quebec, the most frequently searched job titles are:
What job categories do people searching Explainable Ai jobs in Quebec look for? The top searched job categories for Explainable Ai jobs in Quebec are:
Explainable Ai jobs near you
Infographic showing various Explainable Ai job openings in Quebec as of June 2026, with employment types broken down into 86% Full Time, and 14% Part Time. Highlights an 68% In-person, and 32% Remote job distribution.

AI Engineer - Banking Domain

Jay Analytix

Montreal, QC

Contractor

Posted 18 days ago

Job description


Engagement

Contract - 12 months, renewable

Domain

Banking & Financial Services

Locations

Toronto, ON | Montreal, QC | Vancouver, BC | Calgary, AB

Work Model

Hybrid

Start

Immediate


ABOUT THE ROLE

We are looking for an experienced AI Engineer to design and deliver production-grade machine learning and generative AI solutions within a major Canadian bank. You will work across fraud detection, credit risk, regulatory compliance, and customer analytics, partnering with data, engineering, and compliance teams to bring AI from prototype to production.

KEY RESPONSIBILITIES

  • Build and deploy ML and GenAI solutions for banking use cases (fraud, AML, credit scoring, customer analytics).
  • Design LLM-based applications including RAG pipelines and document intelligence for internal workflows.
  • Implement MLOps best practices: model versioning, CI/CD, monitoring, and drift detection.
  • Ensure compliance with OSFI model risk guidelines, PIPEDA/CPPA, and internal governance frameworks.
  • Communicate model performance and business impact to technical and non-technical stakeholders.

MUST-HAVE

  • 7+ years in AI/ML engineering, with 3+ years in banking or financial services.
  • Advanced Python skills: PyTorch/TensorFlow, Scikit-learn, Pandas.
  • Hands-on MLOps experience: MLflow, Kubeflow, Azure ML, or SageMaker.
  • LLM/GenAI development: OpenAI, Azure OpenAI, LangChain, RAG architectures.
  • Cloud proficiency: Azure (preferred), AWS, or GCP.
  • Knowledge of OSFI E-23 model governance, PIPEDA, and explainable AI for audits.
  • Experience with SQL and distributed data platforms (Spark, Databricks, or Snowflake).

GOOD TO HAVE

  • Azure AI-102, AWS ML Specialty, or Google Professional ML Engineer certification.
  • Exposure to IFRS 9, Basel III, or Open Banking frameworks.
  • Experience with real-time ML inference (Kafka, Flink).
  • Bilingual English/French (asset for Montreal).
  • FRM or CFA designation as a complement to technical skills.

HOW TO APPLY

Submit your resume, preferred location, and available start date. Canadian work authorization required.


Employment Type: CONTRACTOR

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