EnCharge AI is looking for an LLM Inference Deployment Engineer to optimize, deploy, and scale large language models (LLMs) for high-performance inference on energy-efficient AI accelerators. This role involves working with AI frameworks, model optimization, and runtime execution to ensure efficient model execution and low-latency AI inference, utilizing various tools and techniques for model deployment and performance tuning.
About the role
About the Role
EnCharge AI is seeking an LLM Inference Deployment Engineer to optimize, deploy, and scale large language models (LLMs) for high-performance inference on its energy efficient AI accelerators. You will work at the intersection of AI frameworks, model optimization, and runtime execution to ensure efficient model execution and low-latency AI inference.
Responsibilities
Deploy and optimize LLMs (GPT, LLaMA, Mistral, Falcon, etc.) post-training from libraries like HuggingFace
Utilize inference runtimes such as ONNX Runtime, vLLM for efficient execution.
Optimize batching, caching, and tensor parallelism to improve LLM scalability in real-time applications.
Develop and maintain high-performance inference pipelines using Docker, Kubernetes, and other inference servers.
Qualifications
Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field.
Experience in LLM inference deployment, model optimization, and runtime engineering.