Job Description

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