Director of AI Engineering – Generative AI
2 weeks ago
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.
- 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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