The Platform 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 the company's Sovereign Cloud and hybrid/multi-cloud environments. The engineer will enable enterprise-grade AI adoption for over 200 government entities.
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Key Responsibilities:
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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.
- Deliverables: Fully operational GPU-based AI infrastructure, GPU Cluster Uptime and Performance Utilization, Reduction in Cost per Training/Inference Workload.
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GPU Cluster Operations: Install, configure, and optimize core components: CUDA, cuDNN, NCCL, NVIDIA Drivers, and GPU Operators. Implement GPU partitioning, scheduling, and performance tuning.
- Deliverables: High-availability architecture for all AI workloads, complete documentation, and runbooks.
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OpenShift AI (RHODS) Management: Deploy, configure, and maintain the Red Hat OpenShift AI (RHODS) platform for multi-tenant use.
- Deliverables: Production-ready OpenShift AI (RHODS) platform, AI Project Onboarding Speed.
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LLM & Model Serving: Build and manage infrastructure for hosting and serving open-source LLM frameworks and supporting RAG pipelines, LoRA adapters, and Vector Databases.
- Deliverables: Multi-model LLM serving environment for entities, MLOps Pipeline Success Rate and Deployment Frequency.
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MLOps & Automation: Implement Infrastructure as Code (IaC) and GitOps for the automated lifecycle management of the AI platform.
- Deliverables: Infrastructure automation via Terraform & Ansible, Automation Coverage for AI Infrastructure.
Required Qualifications & Experience:
- 7–12 years in Cloud Infrastructure, DevOps, ML Infrastructure, or Platform Engineering.
- Deep Hands-On Expertise with GPU Systems (NVIDIA A100/H100), Linux, Containers, and Kubernetes.
- Experience with OpenShift AI (RHODS) or equivalent Kubernetes GPU orchestration.
- Familiarity with LLM Hosting and supporting Vector Databases.
Essential Skills & Competencies:
- Technical: Deep understanding of GPU compute, HPC architectures, and ML performance profiling.
- 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
Work Conditions:
- Full-time, on-site position.
