Python Developer for RAG Pipeline

We are seeking an experienced Python developer to build a RAG pipeline using Qdrant, ChromaDB, and FastAPI. The project involves integrating vector databases and knowledge bases, with a focus on embeddings and RAGAS. The ideal candidate will have experience with Docker and restic for deployment and backup. This is a part-time role with a medium project scale, expected to last 1 to 3 months. Python Developer — RAG Pipeline, Qdrant, ChromaDB, Embeddings, Vector Database, Knowledge Base, RAGAS, FastAPI, restic, Docker Building the knowledge layer for a local-first enterprise AI platform. Need a Python developer to build a full RAG pipeline, structured Knowledge Base, and backup system from scratch. All in Docker, fully replicable. Scope: - Document ingestion: PDF/DOCX loading, chunking, embeddings via Ollama - Vector storage in Qdrant or ChromaDB with metadata per document (area, owner, language, confidentiality, permissions) - RBAC retrieval: users only access permitted documents - Base user context: user_id, role, departments, language, session memory - RAGAS evaluation pipeline - FastAPI endpoints: /ingest and /query - Logging of all queries and retrievals - restic backups tested with restore Deliverables: - Working RAG pipeline with metadata filtering - RBAC retrieval with 2 test roles - FastAPI endpoints in Docker - RAGAS report - Docker Compose and README Required: Python, RAG pipelines, Qdrant/ChromaDB, FastAPI, Docker, RAGAS, restic Timeline: 3-4 weeks, fixed price To apply: share one RAG example with metadata filtering and your price.