AI & Automation Engineer — Phase-Wise Training Roadmap

📌 Step-by-Step Curriculum

13-Phase Curriculum Progression

A structured phase-wise roadmap engineered to transform traditional RPA developers into AI Automation Engineers through practical, build-first modules.

Want to view the full PDF document? Explore Detailed Course Curriculum

Step 0: Absolute Zero Setup

Tailored for Indian IT Non-Coders

Zero-Coding Starter Pack: Python & VS Code Installation Guide

Never written a single line of code in your career? Don't worry! We start from absolute ground zero by downloading Python and VS Code step-by-step from official websites, setting up your environment, and running your very first program.

1 Install Python

Visit python.org/downloads and download Python 3.x for Windows.

⚠️ Must Check: "Add python.exe to PATH" before clicking Install Now!

Verify in CMD: python --version

2 Install VS Code

Download Visual Studio Code from code.visualstudio.com. Install the official Microsoft Python extension (Ctrl+Shift+X).

Select Interpreter: Ctrl+Shift+P → Python

3 First 1 Line of Code

Create folder my_automation and file app.py in VS Code.

              print("Hello Automation!")
            

Click ▶ Run to see live output!

4 Non-Coder Analogy

Think of Python Variables as Excel cells, Lists as Excel rows, and Functions as reusable UiPath Sub-workflows!

💡 Simple English • 100% Practical

Phase 01 Mindset

Transformation Mindset (Why Move to AI & Python?)

Step-by-Step Learning Topics

Session 1: Why Transform? — Evolution of RPA industry, why traditional drag-and-drop bots (UiPath/AA) reach limitations, and the AI + Python shift.
Session 2: Skill Map — Comparing Old RPA Developer vs New AI Automation Engineer architecture & career roadmap.
Session 3: Real Business Use Cases — How AI agents, intelligent document processing, and decision workflows work in top enterprises.

Practical Activity

Take a traditional RPA process (e.g. PDF data extraction) and redesign it using Python + AI to see 10x speed & accuracy gains.

Phase 02 Core Foundation

Python Fundamentals for Non-Coders (Step-by-Step Scratch to Builder)

Part A: Setup & First Syntax

Session 4: Developer Setup

Windows Command Prompt navigation, folder creation, environment variables & file paths.
Session 5: Python Installation

Download Python 3.x, configure PATH environment variable, and test terminal commands.
Session 6: VS Code Mastery

Install VS Code, select Python interpreter, setup project folders & extensions.
Session 7: First Python Code

Syntax basics, comments, variables, strings & numbers with customer invoice examples.
Session 8: Data Types (Excel Analogy)

String, Integer, Float, Boolean, Lists & Dictionaries data structures.
Session 9: Conditions

If/Else business decision logic for automated invoice payment approvals.

Part B: Automation Logic & Integrations

Session 10: Loops

Loops to automate batch data processing across customer transaction records.
Session 11: Reusable Functions

Reusable modular functions (Comparing UiPath Invoke Sub-workflow to Python functions).
Session 12: File Processing

Read and write TXT, CSV, and JSON business data files automatically.
Session 13: Error Handling

Exception handling workflows to make automations 100% crash-proof in production.
Session 14–15: Packages & Project Structure

Installing external libraries via pip & structuring clean multi-file automation projects.
Session 16: REST APIs

HTTP GET and POST REST API calls to fetch live business data without UI clicks.

Practical Hands-On Exercises

Build 5 mini automation scripts: 1) Employee Payroll Formatter, 2) Transaction Rule Approver, 3) Invoice Loop Processor, 4) Automated CSV/JSON File Manager, and 5) REST API Currency Converter.

Phase 03 Source Control

Git & GitHub Fundamentals

Step-by-Step Learning Topics

Session 17: Git Basics — What is Git vs GitHub? Why developers save code versions instead of copying folders.
Core Commands — Learn git init, git status, git add, git commit, and git push.
Professional Setup — Creating clean .gitignore files & writing clear README.md documentation.

Practical Activity

Initialize a local Git repository for your Python project, commit your code, and push your repository live to GitHub.

Phase 04 AI Coding

AI-Assisted Development & "Vibe Coding"

Step-by-Step Learning Topics

Session 18: AI Coding Concept — Prompting AI tools to generate, explain, and refactor Python code without memorizing syntax.
Session 19: Google AI Tools — Using Google AI coding assistance to generate utilities and fix bugs fast.
Session 20: Vibe Coding Workflow — 4-Step Cycle: Prompt AI → Run Code → See Error → Ask AI to Fix.
Session 21: VS Code AI Extensions — Installing AI extensions for smart code completion and automated inline debugging.

Practical Activity

Build a complete Python automation script primarily using AI prompts, testing the code, and using AI to fix any runtime errors.

Phase 05 Initial Phase Capstone

Terminal Mastery & Initial Phase Mini-Project

Command Line Fundamentals (Sessions 22–24)

Session 22: Terminal Basics — Command Prompt shortcuts: cd, dir, mkdir, copy, del, and absolute/relative file paths.
Session 23: Executing Python — Running python app.py directly from terminal without clicking VS Code play button.
Session 24: AI + Terminal — Asking AI to write terminal commands to setup project folders & environments automatically.

Session 25: Initial Phase Mini-Project

Business Problem — Build an automated CSV Invoice Processor from scratch.
Core Requirements — Read customer invoice CSV → Validate payment rules with if/else → Process using loops → Handle errors with try-except → Output JSON summary report.
AI Assistance — Use Vibe Coding workflow to generate and refine project components.

Initial Phase Outcome

Complete your very first end-to-end Python + AI automation project, run it from the command line, and push it to GitHub with total confidence!

Phase 06 LLM Core

Generative AI & LLM Foundation

Objective & Topics

Tokens, context window, temperature & hallucinations
System/user prompts, few-shot & structured output
Validation & Python + LLM API integration

Practical Activity

Build an AI summarizer, document extractor, classifier and decision workflow.

Phase 07 Multi-Agent

Agentic AI with CrewAI

Objective & Topics

Agent roles, goals, backstories, tasks & outputs
Crews, custom Python/search/API tools
Sequential workflows & manager/worker architecture

Practical Activity

Build Researcher → Analyst → Report multi-agent workflow with custom tools.

Phase 08 Web Automation

Intelligent Browser Automation

Objective & Topics

Playwright setup, browser/page, locators & navigation
Click, type, select, uploads, downloads & smart waits
Assertions, screenshots, contexts & AI + Playwright

Practical Activity

Build AI-controlled browser automation where AI decides and Playwright executes.

Phase 09 Frameworks

LangChain Framework

Objective & Topics

Models, prompt templates, chains & structured output
Tools, tool calling, agents & reasoning patterns

Practical Activity

Build reusable prompt pipeline, document chain, Python tool and research agent.

Phase 10 State Machine

LangGraph Workflows

Objective & Topics

State, nodes, edges, START/END & graph architecture
Conditional routing, validation, loops & human-in-the-loop
Persistence, checkpoints & context propagation

Practical Activity

Build Analyze → Decision → Process/Human Review stateful graph workflow.

Phase 11 Integration

AI Automation Integration

Objective & Topics

Technology selection (RPA, Python, APIs, LLMs, Agents, Playwright, LangGraph)
Hybrid enterprise solution architecture design

Practical Activity

Redesign a legacy RPA process using the optimal combination of modern technologies.

Phase 12 Enterprise

Enterprise Engineering

Objective & Topics

Logging, retries, exception handling & fallbacks
Human escalation, .env secrets & credential safety
Prompt testing, agent testing & Git branching

Practical Activity

Add production controls, security, automated tests and delivery practices.

Phase 13 Capstone Final Solution

Build the AI Automation Engineer Solution

Capstone Integration Journey

Business problem framing → Requirements & Tech Stack Selection
Python Core + LLM Prompts + Autonomous Agents & API Tools
Playwright Web Automation + Validation, Logging, Git Testing & Live Demo

Capstone Outcome

Demonstrate complete AI Automation Engineer capability with a live portfolio project.

Complete 60-Module Roadmap

Phase-Wise Detailed Modules Breakdown

Comprehensive step-by-step module breakdown mapping concepts, hands-on exercises, and target outcomes across all 60 masterclass modules.

Showing 60 of 60 Modules

#01 Foundation

Why Transform?

RPA industry evolution, limitations of traditional RPA, AI + Automation convergence, AI Engineer vs RPA Developer.

Hands-on: Analyze a traditional RPA process and identify what AI/Python can improve.

Outcome: Understand why transformation is necessary.

#02 Foundation

RPA → AI Engineer

New developer skill map, Python, APIs, LLMs, Agents, Git, VS Code, Cloud, and AI frameworks.

Hands-on: Compare Old RPA Developer vs AI Automation Engineer architecture.

Outcome: Understand target career/skill path.

#03 Foundation

Real-World AI Automation

Real business use cases, AI agents, document processing, web research, decision automation, and orchestration.

Hands-on: Design one AI automation use case.

Outcome: Think beyond bots.

#04 Environment

Developer Setup

Windows basics, terminal, folders, files, and environment variables.

Hands-on: Navigate folders and execute commands.

Outcome: Become comfortable with developer environment.

#05 Python

Python Installation

Python installation, PATH, pip, Python version, and virtual environment.

Hands-on: Install Python and execute first program.

Outcome: Python environment ready.

#06 Python

VS Code

VS Code installation, extensions, terminal, Explorer, Python extension, and interpreter selection.

Hands-on: Create first Python project.

Outcome: Understand VS Code.

#07 Python

First Python Program

Syntax basics, comments, variables, strings, numbers, and Boolean.

Hands-on: Create simple employee/invoice/customer program.

Outcome: Understand Python syntax.

#08 Python

Data Types

String, integer, float, Boolean, list, tuple, dictionary, and set.

Hands-on: Build customer/order data structure.

Outcome: Understand Python data.

#09 Python

Conditions

If, elif, else comparison and logical operators.

Hands-on: Create transaction validation program.

Outcome: Build decision logic.

#10 Python

Loops

For, while, range(), break, and continue.

Hands-on: Process multiple transactions.

Outcome: Automate repetitive logic.

#11 Python

Functions

Functions, parameters, return values, and reusable code.

Hands-on: Convert previous programs into functions.

Outcome: Understand modular development.

#12 Python

Files

Read and write TXT, CSV, and JSON files.

Hands-on: Build simple file-processing automation.

Outcome: Work with business data.

#13 Python

Exceptions

Try, except, finally, and custom errors.

Hands-on: Build error-handling workflow.

Outcome: Build reliable automation.

#14 Python

Packages

Pip, libraries, imports, and requirements.txt.

Hands-on: Install and use external Python package.

Outcome: Understand Python ecosystem.

#15 Python

Modules & Project Structure

Python modules, packages, main.py, and reusable utilities.

Hands-on: Create mini automation project.

Outcome: Move from scripts to projects.

#16 Python

APIs

HTTP basics, GET/POST, JSON, headers, and API keys.

Hands-on: Call a public REST API using Python.

Outcome: Understand API-based automation.

#17 Developer Tools

Git Basics

Git, repository, clone, commit, push, and branches.

Hands-on: Create Git repository and commit Python project.

Outcome: Basic source control.

#18 AI Coding

AI-Assisted Development

What is AI coding, Copilot-style development, and prompting coding assistants.

Hands-on: Ask AI to create/modify Python code.

Outcome: Start AI-assisted development.

#19 AI Coding

Google AI Coding

Google AI coding tools, AI-assisted code generation, and debugging.

Hands-on: Build Python utility using AI assistance.

Outcome: Develop faster with AI.

#20 AI Coding

Vibe Coding

Vibe coding concept, prompt → code → run → error → fix cycle.

Hands-on: Build small Python application primarily through AI.

Outcome: Remove fear of coding.

#21 AI Coding

VS Code AI Extensions

Open-source AI coding extensions, local/remote models, and coding agents.

Hands-on: Install and test an AI coding extension.

Outcome: Understand AI-enabled IDE.

#22 Command Line

Terminal Fundamentals

Terminal navigation, folder creation, file management, and paths.

Hands-on: Manage Python project entirely from terminal.

Outcome: Become comfortable with CLI.

#23 Command Line

Execute Python

Executing Python scripts directly from terminal and pip commands.

Hands-on: Create and execute files from terminal.

Outcome: Run Python without IDE.

#24 Command Line

AI + Terminal

Give AI instructions to create files, folders, and Python programs.

Hands-on: Create project → create files → execute → fix errors.

Outcome: Understand AI-driven development workflow.

#25 Command Line

Mini Project

Build CLI-based Python automation.

Hands-on: Create CSV processor from terminal.

Outcome: Complete first end-to-end Python automation.

#26 AI Fundamentals

AI Basics

AI, ML, GenAI, LLM, Agent, and Agentic AI.

Hands-on: Map technologies to business problems.

Outcome: Build AI mental model.

#27 AI Fundamentals

LLM Basics

Tokens, context window, temperature, hallucination, and model selection.

Hands-on: Experiment with prompts.

Outcome: Understand LLM behavior.

#28 AI Fundamentals

Prompt Engineering

System prompt, user prompt, instructions, context, constraints, and output format.

Hands-on: Build structured prompts.

Outcome: Create reliable AI instructions.

#29 AI Fundamentals

Advanced Prompting

Few-shot, role prompting, decomposition, structured output, and validation.

Hands-on: Create production-style prompts.

Outcome: Improve AI reliability.

#30 AI + Python

First AI API

API authentication, request/response, and JSON.

Hands-on: Call LLM from Python.

Outcome: Connect Python to AI.

#31 AI + Python

AI Application

Send text to LLM and process response.

Hands-on: Build Python AI summarizer.

Outcome: Build first AI application.

#32 AI + Python

Structured AI Output

JSON output, schema, parsing, and validation.

Hands-on: Extract customer/invoice information.

Outcome: Build deterministic AI workflows.

#33 AI + Python

AI Automation Pattern

Input → Prompt → LLM → Validation → Action.

Hands-on: Build AI decision workflow.

Outcome: Understand AI automation architecture.

#34 AI Framework

CrewAI Introduction

Why agent frameworks, agents, tasks, and crews.

Hands-on: Create first CrewAI agent.

Outcome: Understand multi-agent concept.

#35 AI Framework

CrewAI Agents

Role, goal, backstory, tools, and expected output.

Hands-on: Create research agent.

Outcome: Build first practical agent.

#36 AI Framework

CrewAI Tasks

Tasks, descriptions, expected outputs, and dependencies.

Hands-on: Create multi-step workflow.

Outcome: Understand agent task orchestration.

#37 AI Framework

CrewAI Tools

Search, Python, custom tools, and API tools.

Hands-on: Connect tools to agents.

Outcome: Build tool-enabled agents.

#38 AI Framework

CrewAI Multi-Agent

Manager/worker pattern, sequential/parallel workflows.

Hands-on: Build 2–3 agent solution.

Outcome: Build multi-agent automation.

#39 Browser Automation

Playwright Introduction

Browser automation vs RPA, installation, browsers, and locators.

Hands-on: Automate simple website.

Outcome: Understand Playwright.

#40 Browser Automation

Playwright Actions

Click, type, select, upload, download, and navigation.

Hands-on: Automate web transaction.

Outcome: Build browser automation.

#41 Browser Automation

Playwright Advanced

Waits, selectors, assertions, screenshots, and browser contexts.

Hands-on: Build robust web automation.

Outcome: Production-style automation.

#42 Browser + AI

AI + Playwright

AI decides → Playwright executes.

Hands-on: Build AI-controlled browser task.

Outcome: Combine AI and automation.

#43 LangChain

Introduction

Why LangChain, models, prompts, chains, and tools.

Hands-on: Create first LangChain application.

Outcome: Understand LangChain architecture.

#44 LangChain

Models & Prompts

Chat models, prompt templates, and structured output.

Hands-on: Build reusable AI pipeline.

Outcome: Create reusable AI components.

#45 LangChain

Chains

Sequential processing and transformations.

Hands-on: Build document-processing chain.

Outcome: Understand chaining.

#46 LangChain

Tools

Tool calling and custom Python tools.

Hands-on: Give LLM access to Python functions.

Outcome: Build tool-enabled AI.

#47 LangChain

Agents

Agent reasoning, tools, and action/observation.

Hands-on: Build research/automation agent.

Outcome: Understand AI agents.

#48 LangGraph

Why Graph Architecture

State, nodes, edges, and workflows vs agents.

Hands-on: Convert linear workflow to graph.

Outcome: Understand agent orchestration.

#49 LangGraph

First Graph

State, nodes, edges, and START/END.

Hands-on: Build basic LangGraph.

Outcome: Create first graph.

#50 LangGraph

Conditional Routing

Conditions, routing, and validation.

Hands-on: Build decision-making workflow.

Outcome: Build controlled agents.

#51 LangGraph

Human-in-the-Loop

Approval, interruption, and human decision.

Hands-on: Add human approval step.

Outcome: Build enterprise-safe agent.

#52 LangGraph

Memory & State

State management, persistence, and conversation context.

Hands-on: Build stateful agent.

Outcome: Understand agent memory.

#53 Integration

AI + Browser + Tools

LLM + LangChain/LangGraph + Playwright.

Hands-on: Build intelligent web automation.

Outcome: Integrate core technologies.

#54 Integration

RPA + AI

When to use RPA, Python, API, AI Agent, and browser automation.

Hands-on: Convert an RPA process into hybrid architecture.

Outcome: Learn technology selection.

#55 Architecture

AI Automation Architecture

Agent, tools, APIs, databases, LLM, and orchestration.

Hands-on: Design enterprise AI automation architecture.

Outcome: Think like AI Automation Engineer.

#56 Production

Logging & Error Handling

Logging, retries, exception handling, and validation.

Hands-on: Add production controls.

Outcome: Build reliable solution.

#57 Production

Configuration & Secrets

Environment variables, API keys, and configuration.

Hands-on: Secure API credentials.

Outcome: Understand basic security.

#58 Production

Testing AI Applications

Unit testing, prompt testing, and agent testing.

Hands-on: Create test cases.

Outcome: Build testable AI automation.

#59 Production

Git & Deployment Basics

Branching, requirements, environment setup, and deployment concepts.

Hands-on: Package project for deployment.

Outcome: Understand delivery lifecycle.

#60 Capstone

AI Automation Engineer Project

Business problem → Python → AI → Agent → Tools → Automation.

Hands-on: Build complete AI automation solution.

Outcome: Demonstrate AI Engineer capability.

AI & Automation Engineer Phase-Wise Roadmap