• development and maintenance of RAGs
• Building multi-agent architectures for AI assistants
•Creating pipelines for document ingestion, OCR, chunking, metadata extraction, and vector-store indexing
• optimizing inference on on-premise GPU
• implementing monitoring and evaluation processes for LLMs, embeddings, RAG systems
• data preparation
• Building multi-agent architectures for AI assistants
•Creating pipelines for document ingestion, OCR, chunking, metadata extraction, and vector-store indexing
• optimizing inference on on-premise GPU
• implementing monitoring and evaluation processes for LLMs, embeddings, RAG systems
• data preparation
• Basic understanding of ML, NLP
• Python (Pandas, NumPy, Scikit-Learn; PyTorch/TensorFlow familiarity)
• Initial experience or strong interest in RAG, NLP
• Git, reproducible code practices
• Python (Pandas, NumPy, Scikit-Learn; PyTorch/TensorFlow familiarity)
• Initial experience or strong interest in RAG, NLP
• Git, reproducible code practices
• Experience with vector DBs (pgvector, Pinecone, Qdrant)
• Basic understanding of BM25, embeddings, reranking models, or text chunking strategies
• MLOps practices
• Coursework or personal projects with transformers, LLM fine-tuning etc
• Kaggle or ML competitions
• Basic understanding of BM25, embeddings, reranking models, or text chunking strategies
• MLOps practices
• Coursework or personal projects with transformers, LLM fine-tuning etc
• Kaggle or ML competitions
📧 Zhanarys.Zhandeldinov@bsbnb.kz [Junior AI as subject]
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