Job Title:  Director | AI / ML | Delhi | Engineering | Hybrid Cloud Engineering

Infrastructure Management: Build and maintain on-premise hardware clusters (NVIDIA DGX, GPU nodes) and storage.

GPU Orchestration: Configure GPU scheduling, resource quotas, and multi-instance GPU (MIG) slices using tools like Run:ai or Kubernetes.

Model Serving Pipelines: Deploy and optimize inference backends such as vLLM, Triton, or TensorRT-LLM.

Platform Operations: Implement containerization, networking, Role-Based Access Control (RBAC), and security monitoring for local AI workflows.

 User Enablement: Provide self-service environments (Jupyter, Kubeflow) for internal data science teams

Qualification & Role Requirements

·        Full time Engineering graduate.

·        Core Stack: Deep Linux administration, Docker, and Kubernetes (K8s) orchestration.

·        Hardware/Drivers: Hands-on experience with NVIDIA CUDA, NCCL, and GPU performance tuning.

·        MLOps/LLMOps: Familiarity with model serving runtimes, MLflow, Kubeflow, or Red Hat OpenShift AI.

·        Languages: Proficiency in Python and infrastructure automation tools (Terraform, Ansible).

·        Background: 4 to 8+ years in DevOps, Site Reliability Engineering (SRE), or AI infrastructure operations