AI/ML ENGINEER

As an experienced LLM Engineer, you will be responsible for leading the deployment and optimization of self-hosted Large Language Models (LLMs). Your expertise in deploying, fine-tuning, and optimizing open-source models like LLaMA, Mistral, and Falcon on on-premise GPU servers will be crucial for this role. Your key responsibilities will include deploying and optimizing self-hosted LLMs for low-latency, high-efficiency inference, setting up GPU-accelerated servers for AI workloads using CUDA, TensorRT, and vLLM, implementing model quantization for efficient memory usage, developing APIs for model inference, automating model deployment, fine-tuning and training models, monitoring system performance, and working with cross-functional teams to integrate LLMs into applications. To excel in this role, you should have at least 3 years of experience in AI/ML model deployment, a strong knowledge of Python, PyTorch, and TensorFlow, hands-on experience with LLM inference frameworks, experience with NVIDIA GPU acceleration, proficiency in Linux, Docker, and Kubernetes, knowledge of security best practices for AI deployments, and experience with distributed computing and API development. Preferred qualifications include experience with on-premise AI infrastructure, familiarity with vector databases for RAG applications, and contributions to open-source AI/ML projects. This is an exciting opportunity for a skilled professional to make a significant impact in the field of Large Language Models and AI deployment.,