AI Infrastructure Engineer

Job Title: On-Premise LLM Inference & GPU Systems Consultant

Location: Charlotte, NC

Interview: Video Interview

Description:

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

Deep expertise as an LLM Systems Engineer or AI Infrastructure Runtime Engineer.

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.