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Strong understanding of machine learning algorithms (e.g., regression, classification, clustering) and deep learning frameworks (e.g., CNN, RNN, Transformer). Expertise in large language models (LLMs), including fine-tuning, prompt engineering, and deployment. Familiarity with Retrieval-Augmented Generation (RAG) systems, vector databases (e.g., Pinecone, Weaviate), and embedding models. Capable of building AI agents to support task automation, reasoning, and integration with external APIs. Knowledge of graph-based AI, including graph neural networks (GNNs), knowledge graphs, and their applications in recommendation systems or network analysis. Proficient in data processing, feature engineering, and embedding techniques for text, image, or multimodal data. Experienced with cloud platforms (e.g., AWS, Google Cloud, Azure) and model deployment pipelines.