Chief AI Officer

Heads up: this posting is for future opportunities rather than one specific open role. If you apply, we'll add you to our candidate network and may reach out when relevant roles come up.

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What the market looks like

We've tracked 12 executive-level AI strategy postings across Canada in the last six months, with concentration in Toronto, Montreal, and Vancouver. The strongest candidates bring a blend of hands-on AI/ML fluency and boardroom credibility: they've shipped AI initiatives end-to-end, can articulate business value in financial terms, and know how to govern risk and talent. Compensation for this cohort ranges from $180K to $330K annually, depending on company stage and scope. Technology, financial services, and professional services firms are leading the hiring.

Job responsibilities

• Own the organization's AI strategy, from competitive positioning through roadmap execution and quarterly business reviews with the board or C-suite

• Lead AI/ML team recruitment, capability building, and retention; set technical standards and foster a culture of experimentation and accountability

• Partner with business units to identify high-impact AI use cases, prioritize investment, and measure ROI across pilots and production deployments

• Drive governance, risk, and compliance frameworks around model governance, data quality, and ethical AI; ensure alignment with regulatory requirements

• Manage vendor relationships and make or buy decisions on platforms, tools, and third-party AI services

• Communicate AI capability, progress, and market trends to executives, boards, and stakeholders; shape organizational readiness and change management

• Integrate AI strategy with existing digital and business transformation initiatives; work across finance, operations, and product teams

Candidate requirements

• 10+ years in AI/ML, data science, or data engineering; at least 5 years in a leadership role (director, VP, or Chief-level) responsible for strategy, team, and P&L

• Hands-on technical literacy: familiarity with model types (LLMs, classical ML, etc.), training and deployment pipelines, and data architecture; not required to code, but must review technical work credibly

• Proven track record shipping AI products or capabilities in production; experience scaling from proof-of-concept to enterprise deployment

• Experience navigating AI governance, model risk, bias, compliance, and ethical considerations in regulated or high-stakes environments

• Board or C-suite presentation skills; ability to translate technical complexity into business impact and secure executive buy-in

• Experience building and leading high-performing technical teams; comfort recruiting, retaining, and developing AI talent in a competitive market