AI Architecture & Technical Leadership

• Define and maintain the enterprise AI architecture, standards, and reference models.

• Provide technical leadership and guidance on AI, machine learning, and generative AI solutions.

• Act as a trusted advisor to engineering teams, product teams, and business stakeholders on AI design decisions.

• Ensure architectural consistency and alignment across AI initiatives and platforms.

Strategy & Execution

• Develop and execute a comprehensive AI architecture strategy aligned with business goals, digital transformation, and enterprise architecture principles.

• Translate business requirements into scalable, secure, and maintainable AI architectures.

• Identify, assess, and mitigate architectural risks, including technical debt related to AI solutions.

Platform & Solution Design

• Design end-to-end AI solutions, including data pipelines, model development, training, deployment, and monitoring architectures.

• Ensure seamless integration of AI capabilities with data platforms, applications, and enterprise systems.

• Support MLOps and LLMOps practices to enable reliable, repeatable, and scalable AI deployments.

Governance, Security & Responsible AI

• Define and enforce best practices for AI governance, ethics, security, privacy, and regulatory compliance.

• Ensure AI solutions adhere to responsible AI principles, including transparency, explainability, fairness, and robustness.

• Collaborate with security, legal, and compliance teams to address AI-related risks.

Technology Evaluation & Innovation

• Stay abreast of industry trends, emerging AI technologies, and vendor offerings.

• Evaluate and recommend AI tools, frameworks, and platforms to enhance enterprise AI capabilities.

• Drive innovation by identifying opportunities where AI can deliver measurable business value.

Collaboration & Enablement

• Collaborate closely with data, platform engineering, MLOps, and product teams.

• Support and mentor technical teams on AI architecture patterns, best practices, and design trade-offs.

• Contribute to building a strong AI community of practice within the organization.

• Bachelor’s or Master degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.

• Several years of experience in software, data, or solution architecture, with significant hands-on experience in AI/ML systems.

• Proven experience designing and deploying AI solutions at scale in enterprise environments.

• Strong understanding of machine learning, deep learning, and generative AI architectures.

• Experience with cloud platforms and AI services (e.g., Azure, AWS, GCP).

• Familiarity with MLOps/LLMOps practices, model lifecycle management, and AI observability.

• Strong communication, influence, and stakeholder management skills.

• Demonstrated ability to balance innovation with enterprise-grade reliability, security, and compliance.

• Bilingualism (French and English) required.

• Flexible benefits options

• Defined Benefits Retirement Plan & Group Registered Retirement Savings Plan (RRSP)

• Employee Stock Purchase Plan & corporate discounts

• Physical Wellness Plan & Supplementary Maternity Plan

• Flextime & California Fridays all year

• Social and community events throughout the year