The AI Optimization Engineer must have strong experience in Python-based machine learning and deep learning , including NumPy, scikit-learn, TensorFlow, PyTorch, and Keras, with hands-on knowledge of supervised and unsupervised learning, neural networks, transformer-based models, NLP, CNNs, and Generative AI concepts. The role requires expertise in AI infrastructure and optimization , including HPC environments, GPU clusters, SLURM workload management, Triton Inference Server, TRTLLM, and model optimization techniques such as pruning, quantization, and distillation for scalable LLM deployment.