Virtual Assistant – AI Course Research & Course Creator

We are looking for a Virtual Assistant to help create a practical, text-based AI Engineer online course. This is mainly a research and AI-assisted content creation job. You do not need to be an AI expert or write everything from scratch. Your workflow will be: Research free courses and exercises → Use AI → Make small edits → Create exercises → Prepare documents → Upload to Systeme.io ( and keep the documents and files to send them to us later ) What you will do 1. Research For each topic: Find free online courses, tutorials, documentation and educational resources. Use reliable and accessible sources. Research the topic and collect the important information. Keep the useful source links. You can use ChatGPT and other AI tools to make the research and writing process faster. 2. Create short lessons The lessons should be short and easy to understand. Do not create long textbook-style content. Use this simple format: Short explanation → Example → Exercise Explain the concept briefly, show an example when useful, and then give the student an exercise to apply what they learned. 3. Create exercises Use AI to help create: Coding exercises Practical tasks Questions Quizzes Small challenges Assignments The student should practice what they have just learned. For coding topics, students should run the code themselves on their own computer. 4. Create practical projects Each module should include a practical project where students apply what they learned. The projects should be realistic but manageable. 5. Prepare the content Organize the finished material into: Lessons Exercises Quizzes Assignments Projects Answers or solutions where appropriate Source links Downloadable files when needed 6. Upload to Systeme.io Upload and organize the finished content inside Systeme.io. This includes: Creating lessons Adding text Adding exercises Uploading files Organizing the modules Checking formatting and links Curriculum Module 1 — AI and Computer Science Foundations Introduction to Artificial Intelligence AI, Machine Learning and Deep Learning Types of AI AI Applications Algorithms and Computational Thinking Data Structures Programming Fundamentals Development Environments Git and GitHub Linux and Command Line Module 2 — Mathematics for AI Algebra Functions Vectors Matrices Linear Algebra Probability Statistics Distributions Correlation Calculus Derivatives Gradients Optimization Module 3 — Python for AI Python Fundamentals Variables and Data Types Conditions Loops Functions Lists, Tuples, Sets and Dictionaries File Handling Error Handling Object-Oriented Programming Modules and Packages NumPy Pandas Matplotlib Data Visualization APIs Testing and Debugging Module 4 — Data Engineering for AI Data Collection Data Sources Data Cleaning Data Transformation Data Preprocessing Exploratory Data Analysis SQL Databases Data Integration Data Pipelines Feature Engineering Data Quality Data Visualization Module 5 — Machine Learning Introduction to Machine Learning Supervised Learning Unsupervised Learning Regression Classification Decision Trees Random Forests K-Nearest Neighbors Support Vector Machines Clustering K-Means Feature Engineering Training and Testing Model Evaluation Cross-Validation Hyperparameter Tuning Machine Learning Pipelines Module 6 — Deep Learning Introduction to Deep Learning Neural Networks Perceptrons Layers Activation Functions Loss Functions Gradient Descent Backpropagation Optimization Regularization Overfitting PyTorch Convolutional Neural Networks Recurrent Neural Networks Transformers Model Evaluation Module 7 — Computer Vision Introduction to Computer Vision Digital Images Image Processing OpenCV Image Classification CNNs Transfer Learning Object Detection Image Segmentation Data Augmentation Vision Transformers Video Analysis Module 8 — Natural Language Processing Introduction to NLP Text Processing Text Cleaning Tokenization Text Representation Word Embeddings Text Classification Sentiment Analysis Named Entity Recognition Transformers BERT Transfer Learning NLP Evaluation Module 9 — Generative AI and Large Language Models Introduction to Generative AI Generative AI Applications Large Language Models Tokens and Tokenization Transformer Architecture Attention Prompt Engineering System Instructions Few-Shot Learning Structured Outputs Function Calling LLM APIs Open-Source LLMs LLM Evaluation Module 10 — Retrieval-Augmented Generation Introduction to RAG RAG Architecture Embeddings Vector Databases Document Processing Document Chunking Information Retrieval Similarity Search Metadata Reranking Retrieval Pipelines RAG Evaluation Module 11 — AI Agents Introduction to AI Agents Agent Architecture Tools and Tool Calling Agent Workflows Planning Memory Multi-Step Tasks Agentic RAG Multi-Agent Systems Agent Frameworks LangGraph LlamaIndex Agent Evaluation Human-in-the-Loop Systems Model Context Protocol Module 12 — Fine-Tuning and Advanced AI Model Fine-Tuning Pre-Trained Models Transfer Learning Instruction Tuning Dataset Preparation Fine-Tuning Workflows LoRA QLoRA Quantization Model Compression Local AI Models Inference Optimization Model Evaluation Module 13 — AI Engineering and Deployment AI Engineering AI Application Architecture REST APIs FastAPI Backend Development Model Serving Databases Docker Cloud Computing AI Deployment CI/CD Testing Logging Monitoring Scaling Module 14 — MLOps and LLMOps Introduction to MLOps ML Lifecycle Experiment Tracking Model Versioning Data Versioning Model Registry ML Pipelines Automated Training CI/CD Model Deployment Model Monitoring Model Drift LLM Evaluation LLM Monitoring Cost Optimization Module 15 — AI Ethics and Security Responsible AI AI Ethics Bias and Fairness Transparency Explainability Privacy Data Protection AI Governance AI Security Prompt Injection Data Poisoning Adversarial Attacks Model Security Secure AI Development AI Regulation Module 16 — AI System Design AI System Architecture Requirements Analysis Model Selection Data Architecture Database Selection Vector Database Architecture API Architecture AI Application Architecture Scalability Performance Reliability Security Cost Optimization Monitoring Production AI Systems Important The course should be short, practical and easy to follow. Do not create unnecessary long explanations. The main principle is: Learn → Practice → Build Use free educational resources for research and use AI to help create original, simplified explanations and exercises. Do not copy large amounts of copyrighted material. Ideal candidate Good English Good online research skills Comfortable with ChatGPT and AI tools Organized Good at creating documents Comfortable learning new platforms Basic technology knowledge Systeme.io experience is a plus Basic Python or AI knowledge is a plus You do not need to be an AI expert. Budget Maximum budget: 500 USD for the complete project. This is an AI-assisted research and content organization role. To apply Please send: 1. Short introduction 2. Experience with AI tools 3. Research experience 4. Experience creating educational content if any 5. Systeme.io experience, if any 6. Example of previous work 7. Estimated completion time The goal is simple: research good free resources, use AI to create short lessons, create practical exercises, organize everything and upload it to Systeme.io.