Ciena
Technology
R&DAIPlatformEngineer/Administrator
Neural analysis suggests this role is
optimal for Junior candidates.
“R&D AI Platform Engineer / Administrator at Ciena. Skills: LLM serving, AI infrastructure, Observability. The R&D AI Platform Engineer/Administrator will design, deploy, and manage on-premises AI infrastructure using Kubernetes and Docker to host open-source models. This role involves implementing and optimizing model serving solutions, configuring AI application stack components, and ensuring adherence to enterprise security policies. The engineer will collaborate with architects and product te”
What You'll Achieve.
Deliver scalable platform capabilities and enable internal users to effectively utilize AI platform services.
Industry & Context.
network security; container security; data privacy
Must be authorized to work in Canada.
What They're Looking For.
Must Have
Requires 3+ years of experience developing and administering on-premises AI/ML platforms, utilizing Kubernetes, Docker, and Linux environments. Must have experience with LLM serving frameworks (e.g., SGLang, vLLM), orchestration layers (e.g., LiteLLM), vector databases (e.g., Milvus), and observability solutions (e.g., Langfuse, Prometheus/Grafana). Proficiency in network security, container security, and data privacy practices is essential, along with the ability to translate technical constraints into business outcomes.
Nice to Have
Experience with Atlassian tools (Jira, Confluence, Bitbucket), private cloud architectures (OpenStack), support of design workstations, or CI/CD pipeline environments is preferred. A Bachelor's degree in Computer Science, Software Engineering, Electrical/Computer Engineering, or Math/Stats is also desirable.
What You'll Do.
The R&D AI Platform Engineer/Administrator will design, deploy, and manage on-premises AI infrastructure using Kubernetes and Docker to host open-source models.
This role involves implementing and optimizing model serving solutions, configuring AI application stack components, and ensuring adherence to enterprise security policies.
The engineer will collaborate with architects and product teams to deliver scalable platform capabilities and provide documentation for internal users.
How You'll Work.
Team & Collaboration
Collaborate with AI architects and product teams to identify requirements and deliver scalable platform capabilities.
Communication Scope
documentation
Process & Methodology
Agile
Full Job Description
As the global leader in high-speed connectivity, Ciena is committed to a people-first approach. Our teams enjoy a culture focused on prioritizing a flexible work environment that empowers individual growth, well-being, and belonging. We’re a technology company that leads with our humanity—driving our business priorities alongside meaningful social, community, and societal impact. Ciena is a global leader in high-speed connectivity, enabling innovation across optical networks, routing and switching, and network control systems. This role contributes to the "AI for Engineering" vision by advancing secure, on-premises AI capabilities for product development. The position plays a critical role in establishing and scaling an internal AI platform that supports innovation across Global R&D. **How you will make an impact:** * Design and deploy on-premises AI infrastructure using Kubernetes and Docker to host open-source and open-weight models * Implement and manage model serving solutions including SGLang to optimize inference performance on shared infrastructure * Configure and maintain AI application stack components including LiteLLM, Open WebUI, Milvus, and Langfuse * Ensure adherence to enterprise security policies to protect intellectual property and maintain compliance with R&D governance standards * Collaborate with AI architects and product teams to identify requirements and deliver scalable platform capabilities * Deliver documentation and guidance to enable internal users to effectively utilize AI platform services **The must haves:** * Experience: 3+ years of experience developing and administering on-premises AI/ML platforms * Utilization of Kubernetes, Docker, and Linux environments (Oracle Linux exposure noted) * Application of LLM serving frameworks such as SGLang or vLLM, orchestration layers including LiteLLM, and vector databases such as Milvus * Implementation of observability solutions for AI workflows including Langfuse or Prometheus/Grafana * Applicat
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