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Extreme Networks

Director of AI Engineering – Generative AI & Autonomous Systems (10033) Seattle, WA

Hybrid Engineering
Added
21 days ago
Location
Type
Full time
Salary
Not Specified

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Related skills

aws gcp llms rag generative ai

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.

    •

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