I want to find someone who writes content for my website! My website is mainly wig products!

We're Hiring: Senior AI Systems Engineer (Programmatic SEO & Content Deduplication) We are an emerging premium DTC brand building a React/Next.js + Headless CMS multilingual independent store. We need a Senior AI Systems Engineer to build an AI-powered programmatic SEO content generation system — when a product goes live, the system automatically generates 10 different types of SEO articles per product (brand story, material deep-dive, how-to guide, FAQ, comparison, styling, trust/EEAT content, buying guide, internal linking support, cultural inspiration), covering diverse Google search intents to drive organic traffic and revenue. We are not looking for a general API-calling developer. We need a system architect who can independently design and implement a content deduplication engine. Responsibilities 1. AI Content Generation System Design & Development · Design and implement a programmatic content generation pipeline powered by LLMs (OpenAI GPT-4o / Claude API) · Build RAG (Retrieval-Augmented Generation) systems with knowledge base and vector database management · Develop differentiated generation templates for 10 content types, ensuring each covers a unique search intent 2. Semantic Deduplication System (Core Responsibility) · Deploy and maintain vector databases (Pinecone / Qdrant / Milvus) · Implement pre-generation vector interception: if cosine similarity between new content and any existing content in the same sub-directory ≥ 0.75, the system automatically rejects generation and triggers an alert · Build multi-language independent vector index architecture (English/Chinese/Japanese, etc.) for language-isolated deduplication 3. System Integration & Architecture · Seamlessly integrate AI content generation with Headless CMS (Strapi / Sanity) · Build a complete closed-loop workflow via Webhook: "Product launch → auto-trigger AI generation → content written to CMS → frontend ISR refresh" · Integrate with existing React/Next.js frontend for auto-updating content 4. SEO Engineering & Revenue Driver · Design automated internal linking strategies to channel long-tail traffic to primary product pages · Embed UTM tracking parameters and conversion attribution logic into generated content · Ensure generated content meets Google EEAT (Experience, Expertise, Authority, Trustworthiness) standards Requirements Must-Have: · 3+ years of backend development experience, at least 1 year of LLM application development · Proficient in Python or Node.js, familiar with async programming and microservices architecture · Deep understanding of RAG pipelines: ingestion → chunking → embedding → indexing → retrieval → LLM reasoning · Production experience with vector databases (Pinecone / Qdrant / Milvus / Weaviate) · Hands-on experience with OpenAI API / Claude API and prompt engineering · Experience integrating with Headless CMS (Strapi / Sanity / Contentful) · Understanding of Webhook event-driven architecture · Fluent in English (reading and writing); able to read and write technical documentation Nice-to-Have: · Experience building Programmatic SEO systems · Familiar with Next.js / React frontend architecture and ISR (Incremental Static Regeneration) · Experience with multi-language content systems · Background in cross-border e-commerce / DTC independent stores · Familiar with n8n / Make automation workflow tools · Can provide GitHub repos or demos of past AI content systems What We Offer · Remote work, flexible hours · Full opportunity to build a system from 0 to 1 · Direct collaboration with the founding team, short decision-making paths · Long-term cooperation and system iteration opportunities How to Apply Please submit: 1. Resume or Upwork/LinkedIn profile link 2. Brief description of past AI system projects (code repos or case study links preferred) 3. A short answer to this question: "Imagine there are already 80 articles in the 'Material & Craftsmanship' content sub-directory. Now you need to generate the 81st. How would you technically ensure that this new article is semantically non-duplicative with the previous 80? Please describe your technical approach."