Director of AI Engineering – Generative AI & Autonomous Systems (10033) Toronto, Canada

Key Responsibilities

Leadership & Vision
• Define the AI engineering vision and long-term roadmap; ensure alignment with business strategy and customer outcomes.
• Build, inspire, and scale a world-class AI engineering team, cultivating a culture of innovation, collaboration, and execution.
• Mentor senior engineers and emerging leaders, raising the technical and leadership bar across the organization.
• Champion responsible AI practices and set quality standards for reliability, ethics, and compliance.
End-to-End Productization
• Drive the full lifecycle of AI systems: from research exploration and prototyping through enterprise-scale production launches.
• Ensure seamless integration of AI into core products, balancing cutting-edge innovation with pragmatic delivery.
• Establish and enforce best practices for deployment, monitoring, and lifecycle management of AI systems in production.
• Measure impact and ensure that AI solutions deliver tangible business value.
Technical Leadership
• Provide architectural direction for scalable AI systems leveraging LLMs, multi-agent systems, and generative models.
• Guide technical decisions, ensuring systems are reliable, secure, and cloud-native.
• Evaluate emerging technologies and frameworks; make informed adoption decisions that strengthen competitive differentiation.
• Maintain enough hands-on involvement to earn respect from engineers, while staying focused on strategic leadership.
Cross-Functional & External Influence
• Partner with product management, engineering, and network experts to define and deliver AI-driven features.
• Communicate strategy, progress, and impact to executives, customers, and partners with clarity and influence.
• Represent the company externally as a thought leader in AI, contributing to industry forums, open-source communities, and customer engagements.

Qualifications

• A degree in Computer Science, Artificial Intelligence, or a related field (or equivalent practical experience).
• Proven leadership track record: 12+ years in AI/ML engineering, including 5+ years in senior leadership roles managing teams and large-scale initiatives.
• End-to-end product launch expertise: Demonstrated success leading AI initiatives from concept through production deployment and adoption at enterprise scale.
• Strategic leadership: Ability to define AI roadmaps, prioritize investments, and align execution with business outcomes.
• Team builder & mentor: Experience scaling teams, developing leaders, and creating a culture of technical excellence.
• Technical credibility: Strong foundation in ML/AI with applied expertise in generative AI, LLMs, RAG, or multi-agent systems; able to guide architecture and evaluate tradeoffs.
• Enterprise-scale delivery: Experience integrating AI into production systems with cloud-native architectures (AWS, Azure, GCP).
• Influence & communication: Exceptional ability to engage executives, engineers, and customers with clarity and impact.

Nice to Have:

• Experience with AI/LLMOps platforms, orchestration frameworks, and lifecycle management.
• Domain knowledge in networking, SD-WAN, or observability.
• Recognized contributions to the AI ecosystem (open-source projects, patents, or industry thought leadership).
• Partnerships with academia, startups, or AI vendors to accelerate innovation.