Director of AI Engineering – Generative AI & Autonomous Systems
Extreme NetworksCloud Networking company
Toronto, CanadaDirector
Norwest Venture Partners
Kleiner Perkins Caufield & Byers
AVI Capital Management
Trinity Ventures
Data & AI
About the role
TL;DR
Lead AI engineering for generative AI and autonomous systems, driving innovation and productization.
- •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.
- •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.
Required skills
LLMsmulti-agent systemsgenerative modelsAWSAzureGCP
Tech stack
LLMsmulti-agent systemsgenerative modelsAWSAzureGCP
