Mid-Senior Full Stack Engineer – AI & RAG

• - Build and own the full-stack development of an AI-powered SEO optimization platform.
• - Develop frontend, backend, APIs, database, and deployment independently.
• - Build SEO optimization features using LLMs, RAG, and knowledge-based retrieval.
• - Design and implement knowledge ingestion, embeddings, vector search, retrieval pipelines, and prompt workflows.
• - Develop features for content analysis, keyword optimization, metadata generation, content improvement, and SEO recommendations.
• - Integrate third-party APIs and relevant SEO data sources.
• - Design reliable backend workflows for LLM and retrieval-based processing.
• - Handle testing, debugging, performance optimization, and production deployment.
• - Work independently, make technical decisions, and take ownership from architecture through delivery.

Qualifications

• - 3–5 years of professional full-stack development experience.
• - Strong hands-on experience with modern frontend and backend development.
• - Practical experience integrating LLMs and AI APIs into real applications.
• - Experience building RAG systems, including embeddings, retrieval, vector databases, chunking, and prompt engineering.
• - Experience with technologies such as React/Next.js, Node.js/Python/.NET, or equivalent.
• - Familiarity with vector databases such as pgvector, Pinecone, Qdrant, Weaviate, OpenSearch, or similar.
• - Strong understanding of APIs, databases, authentication, cloud deployment, and application architecture.
• - Basic understanding of SEO, content optimization, keywords, metadata, and search intent is preferred.
• - Able to take loosely defined requirements and turn them into a working product independently.
• - Strong problem-solving and debugging skills.
• - Comfortable working in a fast-moving startup environment with minimal supervision.