What are the responsibilities and job description for the LLM Systems Engineer position at Technogen, Inc.?
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:
- 5 years expertise as an LLM Systems Engineer or AI Infrastructure Runtime Engineer.
- 5 years hands-on experience with NVIDIA H200 clusters and runtime optimization techniques (KV Cache, prefill/decode).
- 3 years experience 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.