RAG Engineers + AI Developers

About the Role

We are seeking a dedicated RAG Engineer + AI Developer to specialize in building advanced Retrieval-Augmented Generation systems that connect Large Language Models directly to complex enterprise knowledge bases. You will engineer sophisticated document parsing pipelines, semantic chunking strategies, embedding generation workflows, and hybrid search indexes to eliminate hallucinations and deliver precise, factual answers.

Key Responsibilities

• Pipeline Architecture: Build end-to-end RAG pipelines encompassing document ingestion, OCR processing, semantic text splitting, and metadata tagging.
• Vector Search Optimization: Configure, tune, and manage vector databases and hybrid search engines (Pinecone, Qdrant, Weaviate, OpenSearch) for optimal recall and precision.
• Advanced Retrieval Techniques: Implement state-of-the-art retrieval enhancements, including query rewriting, multi-query expansion, hybrid keyword-vector search, and cross-encoder reranking.
• Evaluation & Benchmarking: Establish rigorous automated evaluation frameworks to measure retrieval hit rates, faithfulness, answer relevance, and context precision.
• Application Integration: Integrate optimized RAG retrieval components into customer-facing chat interfaces and enterprise search portals.