[2025 Latest] Roadmap to Becoming an AI Engineer from Scratch

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This article is currently being translated from Japanese to English. Full English translation will be available soon. For complete information, please refer to the Japanese version.

Table of Contents

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Introduction

AI Engineer Learning Roadmap Overview

Phase 1

0-3 months

Foundation

Programming Basics + Math Fundamentals

✓ Python syntax (variables, control flow, functions, classes)
✓ Linear algebra, calculus, probability & statistics basics
✓ NumPy and Pandas usage

Phase 2

3-6 months

Data Analysis

Data Analysis Skills

✓ Data preprocessing & cleansing techniques
✓ Exploratory Data Analysis (EDA)
✓ Data visualization (Matplotlib, Seaborn)

Phase 3

6-9 months

Machine Learning

Machine Learning Fundamentals

✓ Supervised learning (regression, classification)
✓ Unsupervised learning (clustering)
✓ Model building with scikit-learn

Phase 4

9-12 months

Deep Learning

✓ Neural network fundamentals
✓ Understanding CNN, RNN, Transformers
✓ Model implementation with TensorFlow/PyTorch

Phase 5

12+ months

Practice & Job Hunt

Practical Projects + Job Search

✓ Participate in Kaggle competitions
✓ Build portfolio projects
✓ Register with recruitment agencies & prepare for interviews

Skill Acquisition Priority Map

Priority: High

Essential Skills

● Python (NumPy, Pandas)
● Machine Learning Fundamentals
● Linear Algebra & Statistics
● scikit-learn

Priority: Medium

Important Skills

● TensorFlow/PyTorch
● SQL and Databases
● Cloud (AWS/GCP/Azure)
● Docker/Kubernetes

Priority: Low

Nice to Have

● Spark/Hadoop (Big Data)
● MLOps tools (MLflow, Kubeflow)
● Edge AI (TensorFlow Lite)
● Research Paper Reading Skills

Specialization

Specialized Skills

● Computer Vision (OpenCV)
● Natural Language Processing (Hugging Face)
● Reinforcement Learning
● Generative AI (LLMs, Stable Diffusion)

AI Engineer Career Path

Junior

0-2 years

Salary: $50K-$75K

• Python basics
• Data preprocessing
• Basic ML implementation

→

Mid-Level

3-5 years

Salary: $75K-$110K

• Model design & optimization
• Project leadership
• Business problem solving

→

Senior

6+ years

Salary: $110K-$180K+

• Architecture design
• Team management
• R&D of new technologies

With the rapid advancement of AI (Artificial Intelligence) technology, the demand for AI engineers is increasing year by year. This article provides a detailed explanation of the career path to becoming an AI engineer.

What is an AI Engineer

An AI Engineer is a specialized professional who utilizes AI technologies such as machine learning and deep learning to solve business challenges. Main responsibilities include:

Design, development, and operation of machine learning models
Data collection, preprocessing, and analysis
Implementation and optimization of AI algorithms
Analysis of business challenges and proposal of AI solutions

Required Skill Set

Programming Skills

Python is an essential skill. Knowledge of machine learning libraries such as TensorFlow, PyTorch, and scikit-learn is also important.

Mathematics & Statistics Knowledge

Fundamental knowledge of linear algebra, calculus, and probability statistics is required. This is essential for understanding machine learning algorithms.

Business Understanding

Beyond technical skills, the ability to understand business challenges and propose appropriate AI solutions is crucial.

Career Path and Salary

The average salary for AI engineers ranges from $75,000 to $150,000, which is relatively high. Compensation varies based on experience and skill level as follows:

Junior (0-2 years): $50,000 - $75,000
Mid-Level (3-5 years): $75,000 - $110,000
Senior (6+ years): $110,000 - $180,000+

Job Market Trends

As of 2025, AI engineer positions are rapidly increasing in the following areas:

Generative AI Development: Development of large language models and image generation AI such as ChatGPT and Stable Diffusion
DX Promotion: AI utilization in corporate digital transformation
Autonomous Driving: Development of autonomous driving systems using computer vision technology
Healthcare AI: AI applications in medical image diagnosis and drug discovery

Learning Roadmap

Step 1: Foundation (3-6 months)

Master Python programming and mathematical fundamentals. You can learn efficiently by utilizing online learning services and programming bootcamps.

Step 2: Machine Learning (6-12 months)

Learn basic machine learning algorithms and implement them with scikit-learn. Gaining practical experience by participating in competitions like Kaggle is also important.

Step 3: Deep Learning (12+ months)

Become proficient in building deep learning models using TensorFlow or PyTorch. It's essential to work on practical projects such as image recognition and natural language processing.

Recommended Learning Resources

Programming Bootcamps: Intensive skill acquisition possible
Online Learning Platforms: Udemy, Coursera, edX
Technical Books: "Deep Learning from Scratch" series, etc.
Practical Platforms: Kaggle, Google Colab

Job Search Tips

To successfully transition to an AI engineer role, the following points are important:

Portfolio Creation: Publish code on GitHub and showcase your achievements
Define Your Specialization: Have expertise in areas like image recognition, NLP, recommendation systems
Use Recruitment Agencies: Efficiently conduct your job search using AI/IT specialized agencies
Continuous Learning: Stay updated with the latest technologies

Conclusion

While AI engineer is a profession requiring high expertise, it offers the potential for a high-income and rewarding career. Through systematic learning and accumulated practical experience, it's possible to become an AI engineer even without prior experience.

Use the information provided in this article to solidify your career plan. Continuous learning and skill development are the keys to success.

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