AI Engineer: Automated Video Feedback & Content Optimization System

We are looking for a skilled AI Automation Engineer to build an intelligent system that analyzes video content and performance metrics to provide actionable insights for creators. The goal is to automate the feedback loop: our system should "watch" a video, "read" its analytics data and comments, compare this with our internal "Winning Video Knowledge Base", and generate precise recommendations on how to improve the video. Key Responsibilities: Data Integration: Build a pipeline to ingest video files and performance data (views, CTR, retention curves, drop-off timestamps, comments etc.) from platforms. Multimodal Analysis: Implement AI models to analyze video frames, audio (transcripts), and pacing to understand what happens at specific timestamps. RAG Implementation: Develop a Retrieval-Augmented Generation (RAG) system based on our proprietary documents (PDFs, Notion pages, etc.) about viral video structures and storytelling. Insight Generation: Create a logic where the AI identifies why viewers dropped off at "Second 45" by correlating the retention dip with the visual/audio content and comparing it against best practices. Automation Workflow: Design a seamless workflow (n8n, Make) that outputs a structured "Improvement Report." Technical Requirements: LLMs: Deep experience with OpenAI (GPT-4o), Claude 3.5, or Gemini 1.5 Pro. Computer Vision: Experience with video processing or using multimodal models for video understanding. Database: Experience with Vector Databases (Pinecone, Weaviate, or ChromaDB) for the knowledge base. Data Analysis: Ability to work with APIs and process JSON/CSV data from analytics dashboards. To be considered, you must provide: A similar AI automation project you’ve built. Which tools/models you would use for this project. Apply tot his job