Director of AI Engineering - Generative AI & Autonomous Systems

Key Responsibilities

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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.