AI Engineering Roadmap in 90 Days: Master Python, ML & LLMs

Description:

An AI Engineer builds intelligent systems that learn from data. Using Python, ML, and AI tools, they create practical applications like:

• Chatbots – AI that can converse with users, like customer support assistants.
• Recommendation Engines – Systems that suggest products or content, like Netflix recommending movies or Amazon suggesting products.
• Predictive Models – AI that forecasts future events, like estimating product demand next month, predicting which customers might stop using a service, or forecasting website traffic.

 

Month 1 – Python for AI & APIs

Goal:

Learn Python basics, work with data, and start using AI tools to build simple applications.

Week 1 – Setup & Python Fundamentals

• Install Python (venv) and IDE (VS Code/PyCharm)
• Git & GitHub: commits, pushes, branching
• Libraries:
  • NumPy → Arrays, mathematical operations
  • Pandas → DataFrames, cleaning & wrangling
  • Matplotlib / Seaborn → Basic plots & visualizations

Mini-project: CSV data analysis & visualization

Week 2 – APIs & OpenAI

• Python requests, JSON parsing, environment variables
• OpenAI API: chat, embeddings
• Error handling & API rate limits

Mini-project: Fetch API data → summarize with GPT → save to MySQL

Week 3 – Hugging Face & ML Basics

• Hugging Face Transformers: summarization, classification
• Scikit-learn: regression, classification, train/test split, basic tuning

Mini-project: Sentiment analyzer

Week 4 – Combined AI APIs

• Combine OpenAI + Hugging Face in one pipeline
• Write README.md for all projects

Mini-project: GPT-powered Q&A CLI tool

 

 

Month 2 – LLM Tooling & Retrieval-Augmented Generation (RAG)

Goal:

Learn to build AI applications that can remember previous interactions, use external information like documents or websites, and find answers intelligently.

Week 5 – LangChain

• Chains, agents, memory, tools
• Build a GPT chatbot with memory

Week 6 – LlamaIndex & Vector Databases

• LlamaIndex for PDF & website indexing
• Vector DBs: FAISS, Chroma, Pinecone
• Manual RAG pipeline: embed → store → retrieve → answer

Week 7 – Project: PDF Q&A Bot

• LangChain + FAISS integration
• Document logging & debugging
• Basic pytest tests

Week 8 – Project: Website AI Search Tool

• Web scraping + indexing
• API error handling & retries
• Architecture diagram & README

 

Month 3 – Integration & Deployment (Local → Cloud)

Goal:

Learn to deploy AI applications locally and on the cloud (AWS), and integrate them securely with any backend/frontend like Laravel, Angular, or React.

Week 9 – Serving AI Locally

• FastAPI → Create REST API endpoints for AI models
• Flask → Build lightweight API services
• pytest → Test your APIs
• Compare FastAPI vs Flask performance

Week 10 – Dashboards, Docker & Cost-Friendly Cloud

• Streamlit → Build interactive AI dashboards
• Docker → Containerize Python applications for easy deployment
• Google Colab + local LLMs → Cost-effective alternatives to cloud APIs

Week 11 – Cloud Deployment (AWS)

• Set up AWS EC2 and deploy Docker containers
• Configure Nginx + HTTPS (Certbot) for secure access
• Use AWS CloudWatch for monitoring and logging

Week 12 – Backend Integration & Final Projects

• Connect your Python AI APIs with any backend/frontend (Laravel, Angular, React)
• Secure APIs with JWT/OAuth
• Improve performance with async requests & caching

Final Projects:

• AI-powered Chatbot deployed on AWS
• AI-based Product Recommender deployed on AWS