2026-11-09 19:48:43

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Chief AI Architect

Montreal, Canada

Let's take a look at the profile we are looking for:
  • Ambitious people with a passion for this area;
  • Degree in Computer Engineering, Electrical Engineering or similar;
  • Minimum 15 years of experience in enterprise and/or solution architecture, including significant hands-on experience in AI-related domains;
  • Deep expertise in AI technologies, including machine learning, generative AI, agent-based systems, and the enterprise platforms required to operationalize them;
  • Strong understanding of AI governance, ethics, regulatory constraints, and risk management in large organizations;
  • Demonstrated experience designing AI reference architectures, defining enterprise guiding principles, and shaping scalable architectural patterns;
  • Solid knowledge of data platforms, MLOps / LLMOps, and cloud-native AI services such as AWS and Azure;
  • Strong leadership presence with the ability to influence executives and senior peers, challenge stakeholders constructively;
  • Written and verbal communication skills (English and French);
  • Ability to work effectively and manage deadlines;
  • Sense of responsibility, commitment and integrity;
  • Focus on innovation, dynamism, autonomy and teamwork.

Main responsibilities:

  • Establish pragmatic AI principles, guardrails, and reusable patterns that accelerate delivery while ensuring consistency, reuse, and control across the organization;
  • Lead the architectural vision for generative and agentic AI, ensuring solutions are secure-by-design, auditable, and operable at enterprise scale;
  • Define enterprise patterns for agentic systems, including autonomy boundaries, human-in-the-loop controls, lifecycle management, and safe-stop mechanisms;
  • Partner with AI delivery, governance, business, data, and technology stakeholders to drive a coherent enterprise AI approach, clarify decision rights, and translate opportunities into scalable golden paths;
  • Advise executives on AI-related investments, platform and vendor choices, adoption sequencing, and trade-offs between speed, control, and risk;
  • Oversee AI risk management, emerging technology assessment, and architectural escalation for complex or high-risk AI initiatives.