AI Infrastructure Architect

1 day ago

Islamabad, Islamabad, Pakistan beBeeGpu Full time
Job Title:

Kubernetes Orchestration Specialist


Job Description:

We are seeking a skilled Kubernetes Orchestration Specialist to lead the deployment and management of GPU-optimized Kubernetes environments that power AI/ML and hypercomputing workloads.

This role is critical to ensuring scalable, reliable, and high-performance infrastructure across on-premises and hybrid cloud environments.

As a core member of our infrastructure engineering team, you will work at the intersection of container orchestration, GPU resource management, and AI application scaling, enabling large-scale distributed training and inference across GPU clusters.

  • Deploy and manage production-grade Kubernetes clusters tailored for GPU-intensive workloads in AI/ML environments.
  • Build and maintain GPU node pools using NVIDIA Device Plugin, CRI-O, or container runtimes supporting GPU scheduling.
  • Orchestrate containerized distributed model training using Kubernetes and frameworks like PyTorch, TensorFlow, or Hugging Face.
  • Design and implement Kubernetes Operators to automate scaling, health monitoring, and lifecycle management of AI/ML services.
  • Use Helm charts to deploy and scale GPU-accelerated applications across clusters.
  • Integrate observability tools such as Prometheus, Grafana, and Kibana to monitor GPU, memory, and pod resource utilization.
  • Configure and troubleshoot CNI plugins (e.g., Calico, Flannel, Cilium) to ensure high-performance pod networking.
  • Collaborate with DevOps, AI, and infrastructure teams to support hybrid and multi-cloud Kubernetes deployments.

Required Skills & Expertise:
  • Strong experience with Kubernetes (K8s) and container orchestration in production environments.
  • Expertise in managing GPU workloads in Kubernetes using NVIDIA GPU Operator, vGPU, and device plugin configurations.
  • Proficiency with container runtimes such as Docker and CRI-O, and orchestration tools like Helm and Kubernetes Operators.
  • Solid understanding of networking within Kubernetes and service mesh integration (e.g., Istio, Linkerd).
  • Familiarity with hybrid/multi-cloud Kubernetes platforms (e.g., GKE, EKS, AKS).
  • Strong scripting and automation skills (e.g., YAML, Helm templating, Bash, Python).

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