LLM Inference & GPU Systems Consultant
This role focuses on optimizing large-scale LLM inference on NVIDIA H200 GPU clusters within an enterprise private GenAI environment, requiring deep expertise in runtime efficiency and OpenShift AI deployment. The consultant will manage production-grade self-hosted models like Llama while ensuring extreme performance in token generation and KV cache management.
Skills required
About this role
Job Title: LLM Inference & GPU Systems Consultant
Location: Charlotte, NC (Onsite)
Duration: 6+ Months
Must be onsite at client in Charlotte, NC at least 3 days/week
Role Overview:
We are seeking an AI Infrastructure Runtime Engineer to build and maintain large-scale on-prem LLM infrastructure. This is an enterprise private GenAI environment running on NVIDIA H200 GPU clusters and an OpenShift AI deployment ecosystem. You will manage production inference internally, including self-hosting open-source LLMs like Llama. We are focused exclusively on inferencing; this role involves no model training infrastructure or fine-tuning pipelines.
Key Responsibilities
NVIDIA GPU Runtime Optimization: Drive extreme runtime efficiency and optimization for the token generation pipeline. Specifically manage prefill/decode optimization and KV cache management.
Inference Serving: Deploy and manage inference engines including vLLM and TensorRT-LLM.
Hardware Utilization: Optimize GPU throughput tuning, batching strategies, and latency optimization. Manage workload orchestration using RunAI and Kubernetes GPU orchestration.
Model Lifecycle Management: Oversee the complete Hugging Face model lifecycle, including model onboarding, deployment, and retirement.
Platform Operations: Operate and maintain the OpenShift AI ecosystem as the primary container platform for GenAI workloads.
Required Qualifications
8+ years experience working as an LLM Systems Engineer or AI Infrastructure Runtime Engineer.
8+ years hands-on experience with NVIDIA H200 clusters and runtime optimization techniques (KV Cache, prefill/decode).
Proficiency in OpenShift AI and GPU orchestration tools like RunAI.
Strong experience with modern inference frameworks, specifically vLLM and TensorRT-LLM.
Proven track record managing the Hugging Face deployment lifecycle.
