Bangalore, Karnataka
Job Summary
"Role Overview: Design, implement, and optimize Retrieval-Augmented Generation (RAG) systems for enterprise AI applications. Enable LLMs to access and utilize internal knowledge bases, ensuring accurate, context-rich, and verifiable outputs.
Key Responsibilities
"Role Overview: Design, implement, and optimize Retrieval-Augmented Generation (RAG) systems for enterprise AI applications. Enable LLMs to access and utilize internal knowledge bases, ensuring accurate, context-rich, and verifiable outputs.
Key Responsibilities:
- Architect and build RAG pipelines integrating LLMs with enterprise data sources (documents, databases, APIs).
- Develop and maintain retrieval components (vector databases, embedding models, search algorithms).
- Implement data ingestion, indexing, and segmentation for large-scale, multi-modal content (PDFs, DOCX, PPTX, images, etc.).
- Ensure robust security, compliance, and access controls for sensitive enterprise data.
- Optimize RAG system performance, reliability, and scalability for production use.
- Collaborate with data engineers, ML engineers, and business stakeholders to deliver end-to-end RAG solutions.
- Evaluate and improve RAG system accuracy, relevance, and user experience.
- Document processes, architectures, and best practices for knowledge sharing."
Skill Requirements
"Role Overview: Design, implement, and optimize Retrieval-Augmented Generation (RAG) systems for enterprise AI applications. Enable LLMs to access and utilize internal knowledge bases, ensuring accurate, context-rich, and verifiable outputs.
Key Responsibilities:
- Architect and build RAG pipelines integrating LLMs with enterprise data sources (documents, databases, APIs).
- Develop and maintain retrieval components (vector databases, embedding models, search algorithms).
- Implement data ingestion, indexing, and segmentation for large-scale, multi-modal content (PDFs, DOCX, PPTX, images, etc.).
- Ensure robust security, compliance, and access controls for sensitive enterprise data.
- Optimize RAG system performance, reliability, and scalability for production use.
- Collaborate with data engineers, ML engineers, and business stakeholders to deliver end-to-end RAG solutions.
- Evaluate and improve RAG system accuracy, relevance, and user experience.
- Document processes, architectures, and best practices for knowledge sharing."
Other Requirements
RAG specialist
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