Module 2: Becoming an AI Engineer

Module 2: Becoming an AI Engineer – 8-Month Roadmap

Module 2: Becoming an AI Engineer

Your Complete 8-Month Roadmap to AI Career Success

Module 2 of 15 – Career Planning Phase

What Does an AI Engineer Do?

AI Engineer Role Definition

An AI Engineer is a professional who builds AI models using machine learning algorithms and deep learning neural networks to generate business insights and develop applications for sentiment analysis, visual recognition, language translation, and more.

Daily Responsibilities

  • • Design and develop AI models
  • • Analyze large datasets for patterns
  • • Collaborate with data scientists and engineers
  • • Implement machine learning algorithms
  • • Test and optimize model performance
  • • Deploy AI solutions to production

Required Skills

  • • Programming (Python, Java, C++)
  • • Machine Learning fundamentals
  • • Statistics and mathematics
  • • Data manipulation and analysis
  • • Cloud platforms (AWS, Azure, GCP)
  • • Problem-solving and critical thinking

AI Career Paths in 2025

Machine Learning Engineer

Salary: $95,000 – $180,000

Focus on building and deploying ML models in production environments

Data Scientist

Salary: $85,000 – $165,000

Extract insights from data using statistical analysis and ML

AI Research Scientist

Salary: $120,000 – $250,000

Develop new AI algorithms and advance the field

NLP Specialist

Salary: $90,000 – $170,000

Focus on language processing and understanding

Computer Vision Engineer

Salary: $100,000 – $185,000

Specialize in image and video analysis AI

AI Product Manager

Salary: $110,000 – $200,000

Bridge technical teams and business requirements

Why AI Engineering is the Career of the Future

  • • 74% of companies plan to increase AI investment in 2025
  • • AI job postings have grown 300% over the past 3 years
  • • Average AI engineer salary is 40% higher than traditional software engineers
  • • Remote work opportunities are abundant in AI field
  • • Every industry is adopting AI – massive demand across sectors

Skills Assessment

Find Your Starting Point

Take this assessment to understand your current skill level and get a personalized learning path.

1. What’s your programming experience?

2. Mathematics and Statistics background?

3. Data analysis experience?

4. Machine Learning knowledge?

5. How much time can you dedicate weekly?

8-Month AI Engineer Roadmap

1

Month 1: Computer Science Fundamentals & Python

Learning Objectives

  • • Master Python basics and syntax
  • • Understand data structures and algorithms
  • • Learn computer science fundamentals
  • • Set up development environment

Projects & Practice

  • • Build a calculator application
  • • Create data structure implementations
  • • Complete Python coding challenges
  • • Set up GitHub portfolio
Time Investment: 10-15 hours/week | Focus: Foundation Building
2

Month 2: Data Structures, Algorithms & Advanced Python

Learning Objectives

  • • Master advanced Python concepts
  • • Implement complex data structures
  • • Understand algorithm complexity (Big-O)
  • • Learn object-oriented programming

Projects & Practice

  • • Build a web scraper
  • • Create sorting algorithm visualizer
  • • Develop a class-based project
  • • Contribute to open source
Time Investment: 12-18 hours/week | Focus: Programming Mastery
3

Month 3: Version Control, SQL & Data Manipulation

Learning Objectives

  • • Master Git and GitHub workflows
  • • Learn SQL and database management
  • • Introduction to NumPy and Pandas
  • • Basic data visualization

Projects & Practice

  • • Build a data analysis dashboard
  • • Create database-driven application
  • • Collaborate on team project via Git
  • • Data visualization portfolio piece
Time Investment: 15-20 hours/week | Focus: Data Skills
4

Month 4: Mathematics & Statistics for AI

Learning Objectives

  • • Statistics and probability theory
  • • Linear algebra for ML
  • • Calculus fundamentals
  • • Hypothesis testing and confidence intervals

Projects & Practice

  • • Statistical analysis project
  • • A/B testing implementation
  • • Mathematical modeling exercise
  • • Data distribution analysis
Time Investment: 18-22 hours/week | Focus: Mathematical Foundation
5

Month 5: Machine Learning Fundamentals

Learning Objectives

  • • Supervised vs unsupervised learning
  • • Regression and classification algorithms
  • • Model evaluation and validation
  • • Feature engineering and selection

Projects & Practice

  • • House price prediction model
  • • Customer segmentation analysis
  • • Classification project (iris dataset)
  • • End-to-end ML pipeline
Time Investment: 20-25 hours/week | Focus: Core ML Skills
6

Month 6: MLOps & Cloud Deployment

Learning Objectives

  • • Docker and containerization
  • • Cloud platforms (AWS/Azure/GCP)
  • • API development with FastAPI
  • • CI/CD pipelines for ML

Projects & Practice

  • • Deploy ML model to cloud
  • • Build REST API for ML model
  • • Create automated ML pipeline
  • • Set up monitoring and logging
Time Investment: 18-24 hours/week | Focus: Production Skills
7

Month 7: Deep Learning & Neural Networks

Learning Objectives

  • • Neural network architecture
  • • TensorFlow and Keras
  • • Convolutional Neural Networks (CNNs)
  • • Recurrent Neural Networks (RNNs)

Projects & Practice

  • • Image classification with CNNs
  • • Text generation with RNNs
  • • Transfer learning project
  • • Computer vision application
Time Investment: 22-28 hours/week | Focus: Advanced AI
8

Month 8: Specialization & Job Preparation

Learning Objectives

  • • Choose specialization (NLP/CV/RL)
  • • Advanced project development
  • • Interview preparation
  • • Portfolio optimization

Projects & Practice

  • • Capstone AI project
  • • Open source contributions
  • • Technical blog writing
  • • Mock interviews and networking
Time Investment: 25-30 hours/week | Focus: Job Readiness

Portfolio Building Strategy

5 Essential Portfolio Projects

These projects will demonstrate your AI skills to potential employers and showcase your ability to solve real-world problems.

Project 1: Data Analysis & Visualization Dashboard

Timeline: Month 3 | Skills: Python, Pandas, Matplotlib, Streamlit

Build an interactive dashboard that analyzes a real dataset and provides business insights.

What to include: Data cleaning, statistical analysis, interactive visualizations, business recommendations

Project 2: Machine Learning Prediction Model

Timeline: Month 5 | Skills: Scikit-learn, Feature Engineering, Model Evaluation

Create a complete ML pipeline for prediction (housing prices, stock market, etc.)

What to include: Data preprocessing, model selection, hyperparameter tuning, performance metrics

Project 3: Deep Learning Image Classifier

Timeline: Month 7 | Skills: TensorFlow, CNNs, Transfer Learning

Build a neural network that can classify images (medical diagnosis, product recognition, etc.)

What to include: Custom CNN architecture, data augmentation, transfer learning, model deployment

Project 4: Natural Language Processing Application

Timeline: Month 8 | Skills: NLP, Transformers, Sentiment Analysis

Create an NLP system (chatbot, sentiment analyzer, text summarizer)

What to include: Text preprocessing, feature extraction, model training, API deployment

Project 5: End-to-End AI System

Timeline: Month 8 | Skills: Full Stack, MLOps, Cloud Deployment

Build a complete AI application with frontend, backend, and ML components

What to include: Web interface, REST API, database integration, cloud deployment, monitoring

Portfolio Presentation Tips

GitHub Best Practices

  • • Clear, descriptive README files
  • • Well-organized code structure
  • • Detailed documentation
  • • Regular commits with meaningful messages
  • • Include requirements.txt and setup instructions

Project Documentation

  • • Problem statement and solution approach
  • • Dataset description and preprocessing steps
  • • Model architecture and rationale
  • • Results and performance metrics
  • • Future improvements and lessons learned

AI Job Market Analysis 2025

Market Demand

Job Growth Rate +74%
Open Positions 2.3M+
Companies Hiring 89%
Remote Opportunities 67%

Salary Ranges

Entry Level (0-2 years) $70K – $95K
Mid Level (2-5 years) $95K – $140K
Senior Level (5+ years) $140K – $200K
Principal/Staff (8+ years) $200K – $350K

Top Hiring Companies

Tech Giants

Google, Microsoft, Amazon, Apple, Meta

$150K – $350K average

AI Startups

OpenAI, Anthropic, Cohere, Hugging Face

$120K – $300K + equity

Traditional Companies

JPMorgan, Tesla, Netflix, Uber

$100K – $200K average

Interview Preparation

Technical Skills Assessment

  • • Machine Learning fundamentals
  • • Coding challenges (Python, SQL)
  • • System design for ML systems
  • • Statistics and probability questions
  • • Algorithm and data structure problems

Behavioral Interview Topics

  • • Project deep-dives and challenges faced
  • • Collaboration and team experience
  • • Problem-solving approach
  • • Learning from failures
  • • Passion for AI and continuous learning

Networking and Job Search Tips

  • • Join AI communities (Reddit, Discord, LinkedIn groups)
  • • Attend AI conferences and meetups (virtual and in-person)
  • • Follow AI researchers and practitioners on Twitter/LinkedIn
  • • Contribute to open source AI projects
  • • Create technical blog posts about your learning journey
  • • Use platforms: LinkedIn, AngelList, AI-Jobs.net, Kaggle Jobs

Career Planning Project

Create Your Personalized AI Career Plan

Complete this comprehensive project to create a roadmap tailored to your situation, goals, and timeline.

Step 1: Current Situation Analysis

Programming:
Mathematics:
Data Analysis:
ML Knowledge:

Step 2: Goal Setting

Step 3: Learning Schedule

Step 4: Portfolio Planning

Step 5: Accountability & Milestones

Module 2 Assessment

Career Planning Knowledge Check

Test your understanding of AI career planning and the 8-month roadmap.

1. What is the recommended weekly study time for the AI Engineer roadmap?

2. Which month focuses on Machine Learning fundamentals?

3. What is the average entry-level AI engineer salary range?

4. How many essential portfolio projects are recommended?

5. Which skills are covered in Month 6 (MLOps)?

6. What percentage of AI jobs offer remote work opportunities?

7. When should you start building your first portfolio project?

8. What is the AI job market growth rate mentioned in the course?

Module 2 Complete!

You now have a comprehensive roadmap to become an AI Engineer

8
Month Plan
5
Portfolio Projects
$95K+
Target Salary