LLM Inference & GPU Systems Engineer

Hi,

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 Run AI 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
1-3 years' experience as an LLM Systems Engineer or AI Infrastructure Runtime Engineer.
Built a multi-agent AI analytics platform using DeepAnalyze-8B, Gemini and vLLM with LLM inference and execution workflows.
Experience developing scalable ML pipelines and deployment-ready AI architectures at Dassault Syst mes.
Strong foundation in REST APIs, backend development, ML pipelines, and containerized AI application.

Regards,

surekha.v