AI for Senior Software Engineers (www.emadibrahim.com)

A new technical guide, "AI for Senior Software Engineers," targets experienced engineers who want more than API-level familiarity with AI — it promises a first-principles, production-focused walkthrough from math to deployed systems. Authored by a veteran engineer, the guide covers the mathematical foundations (linear algebra, calculus, probability), neural network building blocks, training techniques (backpropagation, gradient descent, Adam, regularization), and the architectures that matter today: CNNs, RNNs/LSTMs, and especially transformers and attention mechanisms that power GPT- and BERT-style large language models. It assumes solid programming and systems experience and is structured to move readers from fundamentals to production-ready knowledge. Significant for the AI/ML community, the guide bridges the gap between research concepts and engineering practice: it addresses scalability, monitoring, deployment patterns, and the tooling ecosystem (PyTorch, TensorFlow, Hugging Face) so senior engineers can design, ship, and maintain robust ML services. It also foregrounds ethics, bias, and responsible design, plus emerging trends toward larger models and system-level implications. For teams looking to avoid black-box dependence on third-party APIs and to make informed architecture, optimization, and operational choices, this guide promises a comprehensive roadmap to build, evaluate, and operate modern AI systems end to end.