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integrating with libraries such as Sentence Transformers for embeddings, FAISS for vector search, or Streamlit/Gradio for prototyping interfaces. Experience with vector databases and semantic search (e.g., Pinecone, Weaviate, or FAISS) to support efficient retrieval in LLM applications. Demonstrated expertise in RAG systems, from building retrieval components to integrating them with LLMs for enhanced reasoning and factuality. Proven track … systems, Haystack for RAG pipelines, or evaluation frameworks like Rouge/BLEU for assessing outputs. Familiarity with data-intensive applications, including processing unstructured text, integrating with databases (e.g., Pinecone, Weaviate), or handling multimodal inputs like images for OCR-enhanced workflows. Experience with other cloud platforms (e.g., AWS SageMaker, Azure ML) alongside GCP, including containerization with Docker and orchestration via Kubernetes. More ❯