Data Scientist – Advanced Analytics
3 days ago
Data Scientist – Advanced Analytics Experience Required: 6+ years total (2+ years relevant in RAG / LLM -based systems) Location: Open / Any Location (India) Compensation: ₹13 – ₹15 LPA Employment Type: Full -time Role Overview: We are looking for an experienced Data Scientist – Advanced Analytics with strong expertise in Python, Large Language Models (LLMs), and Retrieval -Augmented Generation (RAG) systems. The ideal candidate will be responsible for designing, building, and optimizing intelligent retrieval systems that combine LLM reasoning with real -time document understanding Key Responsibilities:Design and implement Retrieval -Augmented Generation (RAG) pipelines and architectures.Develop document retrieval, contextual augmentation, and chunking strategies for large -scale unstructured data.Optimize RAG indexing, retrieval accuracy, and context relevance using advanced evaluation metrics.Implement fine -tuning and prompt engineering techniques to improve retrieval and generation quality.Manage token limits, context windows, and retrieval latency for high -performance inference.Integrate LLM frameworks like LangChain or LlamaIndex for pipeline orchestration.Utilize APIs from OpenAI, Hugging Face Transformers, or other LLM providers for model integration.Perform noise reduction, diversity sampling, and retrieval optimization to enhance output reliability.Collaborate with cross -functional teams to deploy scalable RAG -based analytics solutions. Requirements Programming: Strong hands -on experience with Python. RAG Expertise: In -depth understanding of RAG pipelines, RAG architecture, and retrieval optimization. Vector Databases: Practical experience with FAISS, Pinecone, Weaviate, ChromaDB, or Milvus. Embedding Models: Knowledge of generating and fine -tuning embeddings for semantic search and document retrieval. LLM Tools: Experience with LangChain, LlamaIndex, OpenAI API, and Hugging Face Transformers. Optimization: Strong understanding of token/context management, retrieval latency, and inference efficiency. Evaluation Metrics: Familiarity with Retrieval Accuracy, Context Relevance, and Answer Faithfulness.Good to Have:Experience in MLOps for deploying and monitoring LLM/RAG -based solutions.Understanding of semantic search algorithms and context ranking models.Exposure to knowledge retrieval, contextual augmentation, or multi -document summarization.Master's degree in Computer Science, Artificial Intelligence, Data Science, or related field.
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