AI Infrastructure Engineer (Govt Client)

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 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 the company's Sovereign Cloud and hybrid/multi-cloud environments. The engineer will enable enterprise-grade AI adoption for over 200 government entities.

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.
  • Key Deliverables: Fully operational GPU-based AI infrastructure, GPU Cluster Uptime and Performance Utilization, and 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).
  • Key Deliverables: High-availability architecture for all AI workloads and 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, supporting Data Scientists with Notebooks, Training, and Inference Endpoints.
  • Key Deliverables: Production-ready OpenShift AI (RHODS) platform and 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).
  • Key Deliverables: Multi-model LLM serving environment for entities and MLOps Pipeline Success Rate and Deployment Frequency.

• MLOps & Automation:

  • Implement Infrastructure as Code (IaC) using Terraform and Ansible, along with GitOps for automated lifecycle management of the AI platform.
  • Build robust MLOps pipelines for data preparation, training, evaluation, and monitoring (using tools like MLflow/Kubeflow).
  • Key Deliverables: Infrastructure automation via Terraform & Ansible and Automation Coverage for AI Infrastructure.

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, and Kubernetes. OpenShift AI (RHODS) or equivalent Kubernetes GPU orchestration.
• Strong Experience In: TensorFlow, PyTorch, Hugging Face, Distributed Training (DDP, Deep Speed), and ML Ops Stacks (MLflow, Kubeflow).

Essential Skills & Competencies:

• Technical Skills: 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.

Please share your CV with CTC and Notice details to the email provided.