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Data Scientist – Advanced Analytics

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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