Description
NLP Engineer — Natural-Language Q&A / Semantic Search Engine (Self-Hosted, No LLM API) OVERVIEW I need a natural-language question-answering and semantic search engine built on top of a structured dataset I'll provide, integrated into my app's existing search. A user types a plain-English question and gets a direct, grounded answer plus ranked supporting results. Hard constraint: this must NOT call any generative LLM API (OpenAI, Anthropic, Google, Cohere, etc.) and must not depend on any paid per-query AI service. The system runs on self-hosted / local models only. To be clear about what "no LLM" means here: embedding models, classifiers, NER, and extractive QA models are expected and fine — I use neural embedding models already. What I do not want is a generative chat LLM (or an API wrapper around one) producing the answers. WHAT YOU'LL BUILD - A natural-language query endpoint (FastAPI) that takes a question and returns a direct answer + ranked supporting matches - Query understanding: intent classification + named entity recognition (entity names, identifiers, dates, categories, and other fields relevant to the dataset) - Dense vector retrieval over a knowledge base using Qdrant (I already run Qdrant), with optional hybrid keyword/BM25 search - Answering via extractive QA (span extraction from retrieved passages) and/or deterministic template-filled answers — NOT generation - Cross-encoder reranking for precision - An ingestion + embedding pipeline that builds the index f