VP, AI Engineering

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What the market looks like

We've tracked 53 senior-level AI/ML engineering postings across Canada in the last six months, with the strongest concentration in Ontario, British Columbia, and Quebec. Technology, financial services, and IT services firms are the primary hirers. Median compensation for this cohort lands around $220,000, with top-of-range offers reaching $430,000 for candidates who bring track records of shipping ML systems at scale, leading technical teams through complex transformations, and translating business strategy into engineering roadmaps. The best candidates in this market combine deep hands-on ML expertise with the leadership maturity to operate at the executive table and navigate organizational change.

Job responsibilities

• Own the AI/ML engineering strategy and roadmap, translating business objectives into technical priorities and ensuring delivery of production systems that drive measurable business impact

• Lead and scale a team of ML engineers, data scientists, and infrastructure specialists—hiring, developing talent, unblocking technical challenges, and fostering a culture of rigor and delivery

• Partner with executive leadership to define AI capabilities that align with company strategy, including feasibility assessments, resourcing plans, and business case development

• Drive architecture decisions and technical standards for ML systems, including model governance, data pipelines, MLOps infrastructure, and deployment frameworks

• Build and maintain cross-functional relationships with product, data, and platform teams to ensure AI initiatives integrate cleanly with broader engineering and business operations

• Establish metrics for ML system performance, reliability, and business value; own reporting and iteration cycles to continuously improve outcomes

• Advocate for engineering excellence and best practices—code quality, testing, documentation, and knowledge sharing—across all AI/ML work

Candidate requirements

• 10+ years of experience in ML engineering, data science, or AI-focused roles, with at least 5 years in a leadership position managing technical teams or owning ML systems end-to-end

• Demonstrated success shipping ML models and systems to production at scale—not research or prototypes, but real-world systems that delivered business value

• Deep technical fluency in machine learning fundamentals, model development, training infrastructure, and deployment pipelines; comfortable speaking the language of ML engineers and challenging their work

• Experience leading and building teams; track record of hiring, mentoring, and retaining strong technical talent in competitive markets

• Comfort translating between engineering and business contexts—able to articulate technical constraints and trade-offs to non-technical stakeholders and vice versa

• Track record operating in hybrid or fully remote settings, or openness to hybrid work in the Vancouver area