30 - Day AI Engineer Roadmap

30-Day AI Engineer Daily Timetable

🔹 WEEK 1 – AI + Python + First ML Model

🎯 Goal: Strong fundamentals + First ML project

✅ Day 1 – AI Big Picture

1. AI vs ML vs DL vs GenAI

2. Supervised vs Unsupervised

3. Real-world AI use cases

4. Understand AI Engineer vs Data Scientist

👉 Output:

1. Write notes in Notion

2. Create AI learning GitHub repo

✅ Day 2 – Python Basics (Fast Track for C# Dev)

Focus only on:

1. Variables

2. Lists, Dict

3. Loops

4. Functions

5. Virtual environments

👉 Practice:

1. Write small scripts

2. Compare C# vs Python syntax

✅ Day 3 – NumPy + Pandas

1. Arrays

2. DataFrames

3. Read CSV

4. Filter rows

5. Handle missing values

👉 Practice:

1. Load sample CSV

2. Clean data

✅ Day 4 – Data Visualization

1. Matplotlib basics

2. Plot line, bar, histogram

👉 Practice:

1. Visualize dataset

2. Save notebook to GitHub

✅ Day 5 – Linear Regression

1. What is regression?

2. Train/Test split

3. Model fitting

4. Evaluate model (MSE, R2)

👉 Project 1:
🏠 House Price Prediction

✅ Day 6 (Weekend – 4 hrs)

1. Logistic Regression

2. Classification

3. Confusion matrix

4. Accuracy, precision, recall

👉 Build:
📧 Spam Classifier

✅ Day 7 (Weekend – 4 hrs)

1. Decision Trees

2. Random Forest

3. Compare models

👉 Improve Spam classifier using Random Forest

🔹 WEEK 2 – Core ML + Deployment

🎯 Goal: Real ML workflow + Deployment

✅ Day 8 – Feature Engineering

1. Encoding

2. Scaling

3. Handling imbalance

✅ Day 9 – Model Validation

1. Cross validation

2. Overfitting vs Underfitting

✅ Day 10 – KNN + SVM

1. KNN concept

2. SVM basics

✅ Day 11 – Build ML Pipeline

1. Preprocessing pipeline

2. Model pipeline

✅ Day 12 – FastAPI Basics

1. Create API

2. POST request

3. Return prediction

👉 Convert House Price model into API

✅ Day 13 (Weekend – 4 hrs)

🚀 Deploy ML App

1. FastAPI

2. Streamlit frontend

3. Connect model

4. Run locally

✅ Day 14 (Weekend – 4 hrs)

1. Docker basics

2. Dockerize ML app

3. Push to GitHub

🎉 Now you are officially ML Developer.

🔹 WEEK 3 – Deep Learning + LLM + GenAI

🎯 Goal: Modern AI (2026 demand)

✅ Day 15 – Neural Network Basics

1. What is neuron?

2. Activation functions

3. Forward/Backward pass

✅ Day 16 – Build Simple Neural Network

Use:

1. TensorFlow OR PyTorch

👉 Train simple NN on small dataset

✅ Day 17 – CNN Basics

1. Convolution

2. Image classification

3. MNIST dataset

👉 Build digit recognizer

✅ Day 18 – LLM Fundamentals

1. What is Transformer?

2. Embeddings

3. Vector database

4. RAG

✅ Day 19 – OpenAI + LLM API

Use:

1. Azure OpenAI (recommended for you)

2. Or OpenAI API

👉 Build:
🤖 Simple chatbot

✅ Day 20 (Weekend – 5 hrs)

🚀 Build RAG App

1. Upload PDF

2. Create embeddings

3. Store in FAISS

4. Query from document

✅ Day 21 (Weekend – 4 hrs)

1. Improve RAG

2. Add prompt engineering

3. Add memory

🎉 Now you are GenAI Developer.

🔹 WEEK 4 – AI Engineering + Cloud + Certification

🎯 Goal: Become AI Engineer

✅ Day 22 – Azure AI Overview

Focus on:

1. Azure AI Studio

2. Azure OpenAI

3. Cognitive Services

From Microsoft

✅ Day 23 – AI-102 Exam Syllabus

Download syllabus
Understand:

1. Vision

2. NLP

3. Generative AI

4. Responsible AI

✅ Day 24 – Deploy GenAI App to Cloud

Deploy to:

1. Azure App Service
   OR

2. Docker container in Azure

✅ Day 25 – MLOps Basics

1. MLflow

2. Model versioning

3. CI/CD concept

✅ Day 26 – Monitoring + Logging

1. Logging predictions

2. Model drift concept

✅ Day 27 (Weekend – 5 hrs)

🚀 Final Portfolio Project

Build:
AI Resume Analyzer
OR
AI Interview Question Generator

Stack:

1. FastAPI

2. React or .NET frontend

3. Azure OpenAI

4. Deploy

✅ Day 28 (Weekend – 4 hrs)

1. Polish GitHub

2. Write README

3. Add screenshots

4. Record demo video

✅ Day 29 – LinkedIn Branding

1. Update headline: ".NET + AI Engineer"

2. Post about AI journey

3. Add projects

✅ Day 30 – Certification Plan

Register for:

1. Azure AI Engineer (AI-102)

From Microsoft

Start mock tests.

🧠 After 30 Days You Will Have:

✅ Python fundamentals
✅ 3 ML projects
✅ 1 Deep Learning project
✅ 1 RAG project
✅ 1 deployed AI app
✅ Started Azure AI certification
✅ Strong GitHub portfolio