Physics-informed Neural Networks (PINNs), Graph Neural Networks (GNNs), C, Tensorflow, Pytorch, Python, Java, Machine Learning, C++, Deep Learning, VAEs, GANs, reinforcement learning, Generative AI, Diffusion Models, Low-level programming, Operating-system internals, Bayesian Optimization, evolutionary algorithms, Transformers

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

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

database queries , Databases, Apis, Regression Testing, test automation, software testing, Exploratory Testing, Git, Docker, Distributed Systems, Defect Management, Rest Apis, Test Planning, QA quality engineering, logs, CI CD workflows, modern web applications, backend services, release validation, browser developer tools