AI Infrastructure Engineer
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Key skills for this role
About the Role
Dautom is seeking an AI Infrastructure Engineer to architect, build, and operate high-performance AI infrastructure for advanced AI workloads including LLMs, GenAI, Computer Vision, and MLOps.
Key Skills for This Role
Responsibilities
- Design and implement GPU based compute clusters
- Define reference architectures for LLM hosting, Vector Databases, MLOps, and high performance storage/networking
- 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
- 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
- Support Data Scientists with Notebooks, Training, and Inference Endpoints
- Build and manage infrastructure for hosting and serving open source LLM frameworks
- Support RAG pipelines, LoRA adapters, and Vector Databases (Milvus, pgvector)
- Implement IaC (Terraform, Ansible) and GitOps for automated lifecycle management of the AI platform
- Build robust MLOps pipelines for data prep, training, evaluation, and monitoring
Requirements
- 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
- Experience with 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 (MLflow, Kubeflow)
- Deep understanding of GPU compute, HPC architectures, and ML performance profiling
- Strong skills in IaC (Terraform/Ansible), CI/CD, and OpenShift/Kubernetes operators
Full Job Posting
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.
Key Responsibilities & Deliverables
- Design and implement GPU based compute clusters. Define reference architectures for LLM hosting, Vector Databases, MLOps, and high performance storage/networking.
- 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).
- 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.
- 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).
- 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).
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.
- 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 (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
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