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Seeking Full-Stack AI Developer/Agency for "Human-in-the-Loop" EdTech Content Generation Platform Project Overview We are looking for an experienced Full-Stack AI Developer or a specialized AI development agency to build a robust, AI-powered content generation "wrapper" for our EdTech company. The goal of this platform is to empower our in-house content creators. Instead of the AI acting entirely autonomously, a human content creator will upload foundational materials (PDFs, course overviews) into the system. The platform will then use various AI models and media APIs to generate drafts of course materials (video lectures, podcast-style audio, editable presentations, and mock tests). The human creator will then review, edit, and finalize these assets within the platfo...

Development of a Private AI Chat Platform Based Only on Custom Documents and Videos (Open Source RAG System) I am looking for an experienced AI / LLM / RAG developer to build a private ChatGPT-like platform that answers questions exclusively using files and content uploaded by me. IMPORTANT: The AI must NOT browse the internet. All answers must be generated ONLY from the PDFs, books, videos, and documents uploaded into the system. The goal is to create a closed/private AI knowledge assistant based on proprietary content. Required Features: FRONTEND: * Simple ChatGPT-style chat interface * Basic user authentication/login * Optional chat history * Clean and functional UI ADMIN/BACKEND: * Private admin dashboard * Ability to upload: * PDFs * EPUB/books * DOCX/TXT files * You...

This is a focused 1-day task. The LLM pipeline already exists in Python — I need it containerized with Docker and running live on AWS by end of day. No research, no exploration — come ready to execute. Deliverables: 1. Dockerfile & Docker Compose Write a production-grade Dockerfile for the LLM inference pipeline. Multi-stage if needed, minimal image size, env vars for config and secrets. 2. AWS deployment Deploy the container to AWS (ECS Fargate preferred, or EC2 if GPU required). Expose a working endpoint. Basic IAM role and security group setup. 3. Smoke test + handoff Confirm the endpoint is live and responding. Provide a short Bash script or README so I can redeploy independently. No undocumented magic. YOU'RE A FIT IF: ✔️ You've deployed a containerized ...

I want to put together a quick-and-dirty paper prototype that proves one thing: textual instructions can be fed into an LLM, interpreted, and turned into accurate anatomical motion that I can later visualise in my copilot 3d js etc. The workflow I have in mind is simple on paper yet technically layered. First, the LLM receives short, structured cues (“Raise left arm to 90°”, “Rotate torso 15° right”, etc.). It must parse those sentences, identify the key actions, and output a sequence of joint-level positions. From there, the prototype should pass those positions to a lightweight Vision-AI / pose-estimation module so I can preview the motion as stick-figure frames and sanity-check anatomical correctness. Because this is an early prototype, I’m not ask...