Find work in Canada.
678,472 jobs across our Canadian network. How we count
Jobs in Canada
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Senior Data AI Engineer – GenAI & Agentic AI
K&K Global Talent Solutions INC.
…Large Language Models (LLMs), Cloud Technologies, APIs, and…
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Forward Deployed Engineer – AI, Data Engineering & MLOps / Toronto, ON (Onsite) - FTE
AceStack
MLOps / LLMOps: Model lifecycle management, MLflow, CI/CD, automated deployment, model evaluation, monitoring, rollback, and production support.
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Forward Deployed Engineer (Python/PySpark, LLM)
K&K Global Talent Solutions INC.
…applications, RAG, Agentic AI, cloud platforms, and production-grade MLOps/LLMOps. This role requires close client collaboration, rapid prototyping, technical…
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Data & Analytics Leader/Architect
Epsilon Solutions Ltd.
Knowledge of AI, machine learning, Generative AI, Agentic AI, MLOps/LLMOps, responsible AI, and AI-led business transformation.
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Forward Deployed AI Engineer
Keyrus Canada
📍 Location: Hybrid – Toronto or Montreal, Canada (2 days per week at the office)
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AI Architect / AI Technical Lead
Confiar Services
AI Architect / AI Technical Lead – Python, RAG, LLM & LangChain
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Senior Machine Learning Engineer
TD
Architect scalable MLOps pipelines and AIOps frameworks ensuring model versioning, automated retraining, monitoring, and explainability while maintaining 99.9% availability for…
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Senior Machine Learning Engineer
TD
Architect scalable MLOps pipelines and AIOps frameworks ensuring model versioning, automated retraining, monitoring, and explainability while maintaining 99.9% availability for…
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AI Engineer
Guidepoint
…ensuring the integration of responsible AI and best-in-class MLOps. The AI/ML Engineer will be a primary contributor to building scalable AI/ML capabilities using Databricks and other…
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Senior Manager - Senior AI Architect
KPMG
Experience with AI services, data engineering, advanced analytics, MLOps/LLMOps, observability, and cloud security.
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Principal Data Solutions Architect- AI/ML
K&K Global Talent Solutions INC.
Position: Data & Analytics Solutions Architect- AI/ML
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AI Architect / Toronto, ON (Hybrid) - FTE
AceStack
Define MLOps/LLMOps practices, including deployment pipelines, model and prompt versioning, observability, monitoring, drift detection, rollback strategies, and production readiness.
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Forward Deployed Engineer – AI, Data Engineering & MLOps / Toronto, ON (Onsite) - FTE
AceStack
MLOps / LLMOps: Model lifecycle management, MLflow, CI/CD, automated deployment, model evaluation, monitoring, rollback, and production support.
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Data & Analytics Solutions Architect - Open Source
Tata Consultancy Services
Understanding of MLOps and LLMOps capabilities, including experiment tracking, model registry, feature stores, pipeline orchestration, automated quality gates, and versioning of data,…
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Forward deployed Engineer
Tata Consultancy Services
Experience implementing and operationalizing MLOps / LLMOps capabilities including model lifecycle management, CI/CD pipelines, MLflow, monitoring, deployment automation, rollback, and…
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Forward deployed Engineer
Tata Consultancy Services
Experience implementing and operationalizing MLOps/LLMOps capabilities, including model lifecycle management, CI/CD pipelines, MLflow, monitoring, deployment automation, rollback, and…
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AI Architect
BrainRidge Consulting
â - Familiarity with MLOps/LLMOps/AgentOps practices and observability tools such as MLflow, LangSmith, Langfuse, Arize Phoenix, or Weights & Biases.
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Chief Artificial Intelligence Architect
National Bank of Canada
Solid knowledge of data platforms, MLOps / LLMOps, and cloud-native AI services such as AWS and Azure.
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AI Architect (T & I) (Telework/Hybrid)
CBC/Radio-Canada
Develop robust MLOps/LLMOps pipelines with a constant focus on observability, performance profiling and automated testing.
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AI Architect (T & I) (Telework/Hybrid)
CBC/Radio-Canada
Develop robust MLOps/LLMOps pipelines with a constant focus on observability, performance profiling and automated testing.
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AI Architect (T & I) (Telework/Hybrid)
Radio-Canada
Develop robust MLOps/LLMOps pipelines with a constant focus on observability, performance profiling and automated testing.