12mo ago

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

Canada·TorontoFulltimeexecutive

Data ScienceOtherMachine Learning EngineerDataAi Engineering

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Overview

Introduction At our Extreme, we create effortless networking experiences that empower people and organizations to advance. We are seeking a Director of AI Engineering to lead the design, development,

Technical Tools

Data ScienceOtherMachine Learning EngineerDataAi Engineering

Introduction

At our Extreme, we create effortless networking experiences that empower people and organizations to advance. We are seeking a Director of AI Engineering to lead the design, development, and delivery of our next-generation AI-native systems.

This role requires a proven leader who combines technical depth with organizational vision. You will not only set the direction for AI strategy but also ensure that ideas move from research to scalable, production-ready deployments. Your leadership will drive the successful launch of enterprise-grade AI solutions that transform network design, optimization, security, and support.

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.

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.

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.