AI Infrastructure Engineer
Skills
About This Role
Overview
The AI Infrastructure Engineer is a platform specialist responsible for architecting, building, and operating high-performance AI infrastructure to support advanced AI workloads, including LLMs, GenAI, Computer Vision, and MLOps.
This role will focus 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 SDD’s Sovereign Cloud and hybrid/multi-cloud environments.
The engineer will enable enterprise-grade AI adoption for 200+ government entities.
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.
Fully operational GPU-based AI infrastructure.
GPU Cluster Uptime and Performance Utilization.
Reduction in Cost per Training/Inference Workload.
GPU Cluster Operations
Install, configure, and optimize core components: CUDA, cuDNN, NCCL, NVIDIA Drivers, and GPU Operators.
Implement GPU partitioning, scheduling, and performance tuning for high-end GPUs (e.g., A100/H100).
High-availability architecture for all AI workloads.
Complete documentation and runbooks.
OpenShift AI (RHODS) Management
Deploy, configure, and maintain the Red Hat OpenShift AI (RHODS) platform for multi-tenant use.
Manage the integration of NVIDIA GPU Operator for efficient GPU scheduling and support Data Scientists with Notebooks, Training, and Inference Endpoints.
Production-ready OpenShift AI (RHODS) platform.
AI Project Onboarding Speed.
LLM & Model Serving
Build and manage infrastructure for hosting and serving open-source LLM frameworks (Llama, Falcon, Mistral) and supporting RAG pipelines, LoRA adapters, and Vector Databases (Milvus, pgvector).
Multi-model LLM serving environment for entities.
MLOps Pipeline Success Rate and Deployment Frequency.
MLOps & Automation
Implement IaC (Terraform, Ansible) and GitOps for the automated lifecycle management of the AI platform (node onboarding, scaling, model rollout/rollback).
Build robust MLOps pipelines for data prep, training, evaluation, and monitoring (using tools like MLflow/Kubeflow).
Infrastructure automation via Terraform & Ansible.
Automation Coverage for AI Infrastructure.
& 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, and Kubernetes.
- OpenShift AI (RHODS) or equivalent Kubernetes GPU orchestration.
- LLM Hosting (Llama, Mistral, Falcon, etc.) and supporting Vector Databases/RAG systems.
- Strong Experience In: TensorFlow, PyTorch, Hugging Face, Distributed Training (DDP, Deep Speed), and ML Ops Stacks (ML flow, 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
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