AddSource · 4 hours ago
Senior Infrastructure Engineer (On-Prem & GPU Clusters)
United States
Contract
Remote
Senior Level
7+ years exp
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Hiring Manager
Hardeep Kaur
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Responsibilities
Architect and deploy high-performance computing clusters with multi-GPU support for AI/ML workloads.
Implement and optimize GPU resource scheduling, job queuing, and distributed training setups.
Leverage NVIDIA CUDA to optimize performance for AI/ML models and workloads.
Fine-tune GPU configurations for multi-GPU systems, ensuring maximum throughput and minimal latency.
Build Infrastructure as Code (IaC) solutions using Terraform to automate the provisioning and management of on-premise infrastructure.
Create scalable templates for consistent resource deployment.
Deploy and manage container orchestration systems (e.g., Kubernetes, Docker Swarm) to run scalable GPU-accelerated workloads.
Monitor and troubleshoot issues in distributed systems with tools like NVIDIA DCGM, Prometheus, or similar.
Optimize AI/ML pipelines for distributed training across multi-GPU nodes.
Develop strategies to efficiently utilize NVLink, NCCL, and other NVIDIA technologies.
Set up robust monitoring and alerting systems to track GPU utilization, node health, and workload performance.
Collaborate with MLOps teams to integrate GPU clusters into CI/CD pipelines.
Implement security best practices for sensitive AI/ML workloads in an on-premise environment.
Ensure compliance with organizational policies and industry standards.
Qualification
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Required
7+ years in infrastructure engineering, with at least 5 years of direct experience in GPU-accelerated systems and NVIDIA CUDA.
Proven experience in deploying and managing multi-GPU systems for AI/ML workloads.
Proficiency with NVIDIA CUDA for GPU programming and performance tuning.
Hands-on experience with NVIDIA tools and libraries, including NVLink, NCCL, and cuDNN.
Familiarity with MIG (Multi-Instance GPU) configurations and multi-GPU scaling techniques.
Advanced knowledge of Terraform and scripting languages like Python or Bash for automation.
Proficiency with container orchestration tools like Kubernetes or similar.
Expertise in workload management systems and GPU monitoring tools (e.g., NVIDIA DCGM, Slurm).
Experience in deploying and optimizing distributed training frameworks (e.g., TensorFlow MultiWorkerMirroredStrategy, PyTorch DDP).
Strong understanding of networking, storage, and system architecture for high-performance compute environments.
Strong problem-solving abilities and critical thinking skills.
Excellent communication skills for cross-functional collaboration.
Leadership capabilities to guide junior engineers and manage projects.
Company
AddSource
🌐 Welcome to AddSource (DBA name for VeeRteq Solutions Inc.) - Your Workforce Solution Partner! 🌐 We are excited to introduce AddSource, a premier staffing and HR consulting firm dedicated to helping businesses thrive by connecting them with top-tier talent and providing comprehensive HR solutions.
11-50 employees
Funding
Current Stage
Early StageCompany data provided by crunchbase
