AI & ML

  100 Things an AI Engineer Should Know Foundations Python programming Data structures and algorithms Linear algebra Probability theory Statistics Calculus basics Optimization concepts Matrix operations Vector spaces Numerical computing Programming & Software Engineering Object-oriented programming Functional programming basics Git and version control Linux command line Shell scripting APIs and REST JSON and YAML Debugging skills Unit testing Software design principles Python Ecosystem NumPy Pandas Matplotlib Scikit-learn Jupyter notebooks Virtual environments Pip and package management Async programming Logging Type hints Machine Learning Basics Supervised learning Unsupervised learning Reinforcement learning Classification Regression Clustering Dimensionality reduction Feature engineering Cross-validation Bias-variance tradeoff Deep Learning Neural networks Backpropagation Grad...

 AI is a broad field, so job titles vary depending on whether you’re focused on coding, data, research, or business applications. Here’s a clear breakdown of the main AI-related job roles and what they involve: 🤖 Core Technical AI Roles 6 AI Engineer – Builds and deploys AI systems (chatbots, recommendation engines, etc.) Machine Learning Engineer – Designs, trains, and optimizes ML models Deep Learning Engineer – Works on neural networks (vision, NLP, speech) Data Scientist – Analyzes data and builds predictive models NLP Engineer – Works on language models (chatbots, translation tools) 🧠 Research & Advanced Roles 6 AI Research Scientist – Develops new algorithms and publishes research Computer Vision Engineer – Works on image/video AI (face detection, self-driving) Speech Recognition Engineer – Builds voice assistants Reinforcement Learning Engineer – Works on decision-making AI (robots, games) 🛠️ Data & Support Roles 6 Data Engineer – Builds pipelines to co...

 Yes — you can create an AI agent that behaves like a “10-years experienced engineer” in a specific field, but it will be a simulation of expertise, not a real person. For example, you can build an AI agent that acts like an experienced: Software engineer Data engineer Network engineer Cloud architect DevOps engineer Security engineer How an AI Engineer Agent Works You give the AI: Domain knowledge — documents, manuals, best practices Rules and workflows — how engineers solve problems Tools access — APIs, databases, code execution Memory/context — previous conversations or project history Role prompt — “Act like a senior Azure Data Engineer” Example You could create an AI agent that: Designs ETL pipelines Writes SQL or Python code Troubleshoots Azure issues Reviews architecture Explains networking problems Suggests best practices Technologies Used OpenAI APIs Microsoft Azure AI LangChain AutoGen CrewAI Vector data...