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Explainable Ai Jobs in Ontario (NOW HIRING)

PeoplePilot

Ability to design secure, explainable AI outputs and governance for model versioning and evaluation. #J-18808-Ljbffr

Zafin

AI Knowledge Operations Specialist

Toronto, ON

CA$90K - CA$130K/yr

This role ensures the business knowledge is structured, trusted, current, and optimized for AI agent consumption and enabling accurate and reliable, and explainable AI solutions across client ...

Munich Re

Data and AI Enterprise Architect

Toronto, ON · On-site

Collaborate with Data Science, Engineering, Security, and Risk teams to enable scalable, secure, and explainable AI solutions . * Establish architectural patterns for AI model deployment, monitoring ...

ALS

Geodata Scientist

Waterloo, ON · Remote

Experience with uncertainty quantification, probabilistic modelling, Bayesian methods, or explainable AI techniques. * Familiarity with geophysical, geological, geochemical, remote sensing, and ...

Zafin

AI Evaluation Engineer

Toronto, ON

Ultimately, the role helps ensure AI agent capabilities earn and maintain customer trust by delivering consistent, reliable, and explainable outcomes in production. What Will You Do? * Design and ...

Manulife

AI Engineer - Hybrid

Toronto, ON · Hybrid

Collaborate with Risk, Legal, Compliance, Security, Architecture, and Cloud teams to ensure AI solutions are secure, compliant, explainable, and aligned with enterprise standards. * Support model ...

Zafin

AI Knowledge & Governance Lead

Toronto, ON · On-site

These capabilities ensure AI solutions remain trusted, explainable, reusable, and production-ready across all Functional Delivery Units (FDUs). The role also defines evaluation and compliance ...

AltaML

Manager, Software Engineering

Toronto, ON · On-site

CA$140K - CA$170K/yr

Responsible AI & Quality Assurance * Ensure models and systems are explainable, fair, and auditable throughout the solution lifecycle. * Uphold and champion AltaML's Responsible AI (RAI) guidelines ...

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Explainable Ai information

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 the key skills and qualifications needed to thrive as an Explainable AI specialist?

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 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 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 popular job titles related to Explainable Ai jobs in Ontario?

For Explainable Ai jobs in Ontario, the most frequently searched job titles are:

What job categories do people searching Explainable Ai jobs in Ontario look for?

The top searched job categories for Explainable Ai jobs in Ontario are:

Explainable Ai jobs near you
Infographic showing various Explainable Ai job openings in Ontario as of August 2026, with employment types broken down into 71% Full Time, 25% Part Time, and 4% Contract. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution.

Solutions Architect

PeoplePilot

Full-time

Re-posted 4 days ago

Job description

Strong experience designing and governing multi-agent AI architecture.

Hands-on experience with Vertex AI and GCP AI services.

Strong prompt engineering experience with LLMs such as GPT, Claude, and Gemini.

Experience with LangChain, Semantic Kernel, and AI agent frameworks.

Experience designing RAG pipelines using secure coding knowledge packs.

Strong knowledge of vector databases and embeddings, including Pinecone, FAISS, and Vertex embeddings.

Strong Python experience and experience with ML pipelines.

Experience designing agent memory using vector databases or knowledge graphs.

Experience with LLM evaluation frameworks, including accuracy, false positives, latency, and judge scoring models.

Ability to design secure, explainable AI outputs and governance for model versioning and evaluation.

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