🚀 AI Engineer Preparation Repository

An end-to-end, hands-on roadmap to becoming an AI Engineer by building production-ready AI applications using LLMs, RAG, AI Agents, LangGraph, MCP, and modern AI engineering practices.

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📖 About This Repository

This repository documents my journey of learning and building real-world AI systems from the ground up.

Instead of only studying concepts, I am implementing projects, experiments, notes, and assignments every week to gain practical experience in modern AI Engineering.

The goal is to build production-ready AI applications while developing a strong understanding of the underlying concepts.

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🗺️ Learning Roadmap

✅ Week 1 — LLM Fundamentals + API Mastery

• Large Language Models (LLMs)
• Tokenization
• Context Windows
• Chat Completions
• OpenAI & Groq APIs
• Environment Variables
• Prompt Basics
• API Integrations

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✅ Week 2 — Prompt Engineering + Structured Outputs

• Prompt Engineering Techniques
• Zero-shot
• Few-shot
• Chain-of-Thought
• Role Prompting
• JSON Mode
• Structured Outputs
• Function Calling Basics

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✅ Week 3 — RAG Foundations

• Embeddings
• Vector Databases
• Chunking
• Semantic Search
• Retrieval-Augmented Generation
• Building a Basic RAG Pipeline

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✅ Week 4 — Advanced RAG + Evaluation

• Hybrid Search
• Re-ranking
• Query Expansion
• Context Compression
• RAG Evaluation
• Hallucination Detection
• Performance Optimization

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✅ Week 5 — AI Agents + Tool Use

• Agent Architecture
• Tool Calling
• Multi-step Reasoning
• Memory
• Planning
• External APIs
• Custom Tools

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✅ Week 6 — LangGraph + MCP + Multi-Agent Systems

• LangGraph
• State Machines
• MCP (Model Context Protocol)
• Multi-Agent Workflows
• Agent Communication
• Autonomous Systems

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✅ Week 7 — Observability + Guardrails + Security

• AI Application Monitoring
• Logging
• Tracing
• Prompt Injection Protection
• Guardrails
• Security Best Practices
• Evaluation Pipelines

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✅ Week 8 — Deployment + Fine-Tuning + Capstone

• FastAPI
• Docker
• Deployment
• Model Serving
• Fine-Tuning Concepts
• End-to-End AI Project
• Production Readiness

📂 Repository Structure (As of Now)

AI_Engineer_Codes_Repository/
│
├── week1/
├── week2/
├── week3/
├── week4/
├── week5/
├── week6/
├── week7/
├── week8/
│
├── projects/
├── notes/
├── resources/
│
├── README.md
└── .gitignore

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🛠️ Tech Stack

Languages

• Python

AI & LLMs

• OpenAI
• Groq
• LangChain
• LangGraph
• MCP

Vector Databases

• ChromaDB
• FAISS
• Pinecone

Backend

• FastAPI

Deployment

• Docker

Version Control

• Git
• GitHub

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📈 Progress Tracker

Week	Topic	Status
Week 1	LLM Fundamentals + API Mastery	⏳ In Progress
Week 2	Prompt Engineering + Structured Outputs	⬜ Planned
Week 3	RAG Foundations	⬜ Planned
Week 4	Advanced RAG + Evaluation	⬜ Planned
Week 5	Agents + Tool Use	⬜ Planned
Week 6	LangGraph + MCP + Multi-Agent	⬜ Planned
Week 7	Observability + Guardrails + Security	⬜ Planned
Week 8	Deployment + Fine-Tuning + Capstone	⬜ Planned
Bonus	Interview Prep + Resume Building	⬜ Planned

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🎯 Goals

By the end of this roadmap, I aim to:

• Build production-ready AI applications
• Develop end-to-end RAG systems
• Design AI Agents with tool use
• Build multi-agent workflows
• Deploy scalable AI services
• Create an AI Engineering portfolio

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