Mid-Level Backend / AI Engineer — Archive Knowledge Base, Semantic Search & RAG

FORM is building an archival fashion marketplace that connects rare clothing with historical content such as runway footage, magazine scans, photography, editorial and designer research. We’re looking for a mid-level backend/AI engineer to help build the knowledge and relationship layer behind the platform. You’ll work with our lead full-stack developer and fashion researcher. Responsibilities: • Model relationships between products and archival content • Normalize inconsistent product and archive metadata • Build semantic and hybrid search with PostgreSQL/pgvector • Generate and manage embeddings • Create APIs for retrieval and internal review tools • Develop AI-assisted metadata suggestions with sources and confidence scores • Build a human-approval workflow before generated information is published • Implement background jobs, queues, retries, testing and monitoring • Help develop the foundation for a future RAG research assistant • Explore image, scan and video analysis Current environment: TypeScript/Node.js PayloadCMS PostgreSQL and pgvector Shopify Algolia React-based headless storefront Python Requirements: 3+ years of backend development experience Strong Python or TypeScript/Node.js experience PostgreSQL schema design and indexing Practical experience with embeddings, vector search, semantic search or RAG REST or GraphQL API development Background jobs, webhooks and failure handling Ability to separate sourced facts from AI-generated inferences Clear communication and documentation Comfortable collaborating within an existing architecture Helpful experience: Knowledge graphs or ontology design PayloadCMS Algolia, Elasticsearch or OpenSearch LangChain or LlamaIndex OCR, document processing or multimodal search Shopify integrations AWS or another major cloud platform If the first phase is successful, this can become an ongoing part-time, monthly or larger project-based engagement.