About the Job: The AI Infrastructure Engineer is a platform specialist responsible for architecting, building, and operating high-performance AI infrastructures to support advanced AI workloads, including LLMs, GenAI, Computer Vision, and MLOps. This role focuses on managing GPU clusters (NVIDIA A100/H100), deploying and maintaining Red Hat OpenShift AI (RHODS), and ensuring secure, scalable, and cost-efficient AI platforms across Sovereign Cloud and hybrid/multi-cloud environments. The engineer will facilitate enterprise-grade AI adoption for over 200 government entities.
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Key Responsibilities & Deliverables:
- GPU & AI Platform Architecture: Design and implement GPU-based compute clusters, define reference architectures for LLM hosting, Vector Databases, MLOps, and high-performance storage/networking.
- GPU Cluster Operations: Install, configure, and optimize core components including CUDA, cuDNN, NCCL, and NVIDIA Drivers. Implement GPU partitioning, scheduling, and performance tuning for high-end GPUs (e.g., A100/H100).
- OpenShift AI (RHODS) Management: Deploy, configure, and maintain the RHODS platform for multi-tenant use, integrating the NVIDIA GPU Operator for efficient GPU scheduling and supporting Data Scientists with Notebooks, Training, and Inference Endpoints.
- LLM & Model Serving: Build and manage infrastructure for hosting and serving open-source LLM frameworks and supporting RAG pipelines, LoRA adapters, and Vector Databases.
- MLOps & Automation: Implement Infrastructure as Code (IaC) with Terraform and Ansible and build robust MLOps pipelines for data preparation, training, evaluation, and monitoring.
Required Qualifications & Experience:
- Experience: 7–12 years in Cloud Infrastructure, DevOps, ML Infrastructure, or Platform Engineering.
- Deep Hands-On Expertise: GPU Systems (NVIDIA A100/H100), Linux, Containers, Kubernetes, OpenShift AI (RHODS), LLM Hosting, and supporting Vector Databases.
- Strong Experience In: TensorFlow, PyTorch, Hugging Face, Distributed Training, and MLOps Stacks (MLflow, Kubeflow).
Essential Skills & Competencies:
- Technical: Deep understanding of GPU compute, HPC architectures, and ML performance profiling. Strong skills in IaC (Terraform/Ansible), CI/CD, and OpenShift/Kubernetes operators.
- Soft Skills: Strong troubleshooting, optimization, and performance engineering mindset. Excellent cross-functional collaboration and documentation skills.
Preferred Certifications:
- NVIDIA Deep Learning / AI Infrastructure Certification
- Red Hat OpenShift AI specialization
- Kubernetes CKA/CKAD
- Azure AI or Oracle Cloud AI certifications
- Terraform & Ansible certifications
Language Requirements: Not specified.
