AI Systems Engineer (Full-Stack / Backend / AI Infrastructure)

We are building an AI-powered productivity assistant for founders, executives, and small teams who manage daily work across emails, meetings, documents, notes, voice, and internal tools. This is not a chatbot. The goal is to build a real AI workflow system that turns scattered work into clear actions, summaries, drafts, reminders, and automations. The assistant helps users understand emails, summarize meetings, process documents (including scanned PDFs using OCR), answer questions from private data, generate replies, create tasks, and automate workflows. We are looking for an experienced backend or full-stack engineer to build the core AI system. WHAT YOU WILL BUILD AI chat backend with streaming responses RAG system for documents, PDFs, scanned files, and notes OCR pipeline for scanned PDFs and images Vector database for knowledge search AI agent system for reasoning and task execution Tool integration for email, calendar, and tasks Workflow automation engine (n8n-style AI workflows) Conversation memory system Voice input (speech-to-text) Voice output (text-to-speech) Secure backend architecture KEY CAPABILITIES Summarize emails, meetings, and documents Extract tasks and reminders Answer questions from private knowledge Process scanned PDFs and images using OCR Generate email replies and drafts Run multi-step AI workflows Maintain long-term user memory IDEAL EXPERIENCE Backend development (Node.js, Python, or similar) LLM API experience (OpenAI, Anthropic, Gemini, etc.) RAG system and vector databases AI agents or tool-calling systems API design and backend architecture Streaming systems (SSE or WebSockets) Database design and secure systems NICE TO HAVE LangGraph, LangChain, or custom agents n8n or workflow automation tools Qdrant, Weaviate, Pinecone, or pgvector OCR tools (Tesseract, AWS Textract, etc.) Gmail, Calendar, Slack API experience Speech-to-text and text-to-speech systems AI SaaS experience DELIVERABLES AI backend architecture RAG + OCR system Vector database setup Streaming chat API Tool calling system Memory system Workflow automation engine Voice system (optional) API documentation WORKFLOW Candidate reviews project and gives feedback Short technical discussion Paid trial task Long-term contract (3–6 months) Team expansion into modules WHAT WE LOOK FOR System design thinking Clean backend architecture Real AI workflow understanding Ability to integrate multiple systems Practical engineering mindset WHEN APPLYING Relevant AI/LLM project examples GitHub username for repository access if shortlisted