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• Practical experience with large language models (LLMs), prompt engineering, fine-tuning RAG-based applications, and benchmarking using frameworks like LangChain.
• Strong background in natural language processing (NLP) with experience using spaCy, word2vec, Flair, BERT.
• Formal training in machine learning, including dimensionality reduction, clustering, embeddings, and sequence classification algorithms.
• Proficiency in Python and experience working with ML frameworks like PyTorch, TensorFlow, and Hugging Face Transformers.
• Experience with cloud platforms such as AWS, GCP, or Azure.
• Understanding of data modeling principles and complex data architectures.
• Experience working with relational and NoSQL databases and vector stores (e.g., MySQL, Postgres, Solr, Elasticsearch, OpenSearch).
• Familiarity with distributed computing frameworks like Spark, Scala, or Ray (highly prefer...