RAG / Vector Search Engineer — IXO Careers

RAG / Vector Search Engineer — IXO Careers IXO RAG / Vector Search Engineer — IXO Careers (https://ixolabs.ai/cdn-cgi/content?id=6vEQYcYKj3OzPgu.q7iRIIBXXqCmoB.N2DbpmoH6tEM-1785416729.7977846-1.2.1.1-A_u2y2Y3X_qtP9YpmfS_NoPrVuTjyv.ijAEcutL3H30) RAG / Vector Search Engineer Category: coding Compensation: $90 – $145/hr Employment Type: Remote Locations: USA, UK, Canada, Germany, Australia Skills: RAG, Vector DB, Embeddings Job Description About this role Retrieval-Augmented Generation looks easy in a demo and hard in production, where chunking, hybrid search, and re-ranking decide whether the system actually works. As a RAG / Vector Search Engineer for AI training, you will help AI generate retrieval pipelines that handle real corpora, real queries, and real failure modes. Key Responsibilities • Generate and evaluate instruction-response pairs covering chunking, embeddings, and retrieval design. • Review AI-generated code using pgvector, Pinecone, Weaviate, Qdrant, Milvus, and Vespa. • Provide feedback on hybrid search (BM25 • dense), re-ranking, and query rewriting. • Validate AI handling of evaluation harnesses (Ragas, TruLens) and retrieval metrics. • Evaluate ingestion pipelines, document parsing (PDFs, HTML, OCR), and metadata design. • Identify subtle issues in embedding drift, stale indexes, and adversarial-document risk. Ideal Qualifications • 3• years building production RAG or semantic search systems. • Deep familiarity with at least two vector databases. • Strong grasp of embedding models, distance metrics, and vector index trade-offs (HNSW, IVF, ScaNN). • Experience with hybrid retrieval and modern re-rankers (cross-encoders, ColBERT). • Comfort with Python and at least one orchestration framework (LlamaIndex, LangChain). • Familiarity with on-premises and cloud deployment of vector stores is a plus. Project Timeline • Start Date: Immediate • Duration: Ongoing • Commitment: Flexible, 10-25 hours/week Contract & Payment Terms •