probability , Tensorflow, Deep Learning, Pytorch, Python, Java, Unsupervised Learning, Fourier neural operators, physics-informed neural networks, supervised learning, evolutionary algorithms, calculus, VAEs, DeepONet, Optimization, linear algebra, Graph Neural Networks, GANs, Bayesian optimization, Transformers, generative AI, neural operators, Statistics, diffusion models

Cuda, Git, Pytorch, Kubernetes, Python, Hugging Face, Ray, MLflow, ROCm, vLLM, Kubeflow, Docling, Go

data preparation , model selection , probability , Retrieval-Augmented Generation (RAG) systems, Large Language Models (LLMs), Tensorflow, Deep Learning, Git, Pytorch, Machine Learning, Python, Agents, Reasoning patterns, Optimization, Feature engineering, Production performance considerations, linear algebra, Maintainable code, Orchestration frameworks, Testing strategies