Find AI engineer positions
AI technology is becoming a part of everyday life from voice assistants to smart home devices and beyond. As demand grows, so does the need for AI engineers with specialized skills in machine learning, data analysis, and system development. It’s a high-demand, high-reward career with strong salary potential.
In this article, we’ll explore what AI engineers do, how much they earn, and the skills and certifications that can help you succeed. We’ll also cover how to break into the field, even if you’re starting from scratch. If you're considering a career in AI, this guide is for you.
What is an AI engineer?
AI engineers develop systems and applications that use artificial intelligence to solve real-world problems. They build machine learning models that help machines learn from big data, similar to how humans learn from experience. From voice assistants like Siri to self-driving cars, AI is becoming a part of everyday life and AI engineers are the ones behind the technology.
Why AI engineers are in high demand
Rapid market growth
Japan’s AI market is projected to grow from ¥685.8 billion in 2023 to over ¥2.5 trillion by 2028. Globally, the generative AI market — tools like ChatGPT and Google Bard — is expected to hit $1.3 trillion by 2032.
Use across industries
AI is transforming education, healthcare, entertainment, security, and more. Whether it’s evaluating language tests, supporting elderly care, or flagging credit card fraud, AI is solving real problems efficiently.
Talent shortage
Japan faces a severe shortage of AI professionals — projected to reach 124,000 by 2030. As companies shift toward in-house digital innovation, competition for experienced AI engineers is heating up, along with salary expectations.
Victoria Ryo, Associate Director of Technology at Robert Half Japan, commented on the IT talent shortage in Japan:
"Historically, companies in Japan have outsourced technical work instead of developing it in-house or nurturing internal talent. However, with the acceleration of digital transformation, the demand for experienced professionals who can drive digital innovation internally has surged. As a result, companies are struggling to secure skilled AI engineers, and the talent shortage has become increasingly severe.
In particular, fields such as AI, cloud computing, and cybersecurity are seeing rising salaries, and salary negotiations are becoming more common. Companies are strengthening strategies to attract and retain top talent by enhancing compensation and career path opportunities. This trend is expected to continue, creating a more favorable job market for engineers with specialized skills."
What AI engineers do
AI engineers are responsible for designing and developing AI models, processing and analyzing training data, and maintaining AI systems. Let’s take a closer look at their key tasks.
System design and development
AI engineers build the systems that power AI. Python is the primary programming language used. Later, AI models are integrated into the system, enabling the AI to learn autonomously.
Data collection and processing
AI engineers gather data that the AI will learn from, ensuring it aligns with analysis goals. Raw data often needs cleaning—removing unnecessary information and converting formats—to improve model accuracy, as AI performance heavily depends on the quality of training data.
AI model design and development
Designing and developing AI models involves feeding large datasets into the system so the AI can learn. Engineers choose the right type of model—supervised, unsupervised, or reinforcement learning—and refine data selection to maximize accuracy.
Analysis and evaluation
Engineers assess the AI model’s results, checking accuracy and efficiency. They use evaluation methods like cross-validation and confusion matrices, making improvements to the model or system performance as needed.
Operation and maintenance
After deployment, AI engineers maintain the system over time. They retrain or adjust models when new data becomes available and ensure performance remains optimal, as model accuracy can decline over time.
Related roles
AI engineering overlaps with several other roles:
Salary expectations for AI engineers
See the latest salary benchmarks
According to Robert Half’s Salary Guide, AI engineers in Japan typically earn between ¥6.5M and ¥13.5M, depending on experience and skill level. Related roles like data scientists or data engineers can earn similar or even higher salaries.
Role
| Less Experience
| Average Experience
| Strong Experience
|
Data Engineer / Architect
| ¥7,000,000
| ¥9,000,000
| ¥12,000,000
|
Data Analyst / Consultant
| ¥7,500,000
| ¥10,500,000
| ¥13,500,000
|
Data Scientist
| ¥7,500,000
| ¥10,500,000
| ¥13,500,000
|
Data Science Manager
| ¥10,500,000
| ¥13,500,000
| ¥15,500,000
|
Chief Data Officer (CDO) / Head of Analytics
| ¥14,000,000
| ¥22,500,000
| ¥30,000,000
|
Machine Learning / NLP
| ¥6,500,000
| ¥10,500,000
| ¥13,500,000
|
Product Manager / Software Architect
| ¥7,500,000
| ¥9,500,000
| ¥13,500,000
|
Backend Engineer
| ¥5,300,000
| ¥8,300,000
| ¥12,500,000
|
Frontend Engineer
| ¥6,500,000
| ¥8,500,000
| ¥10,500,000
|
Job Title
| AI/ML Engineer
|
Expected Salary
| ¥10,500,000 – ¥13,500,000
|
Job Description
|
- Design, develop, and maintain AI/ML models and algorithms
- Implement blockchain solutions and integrate them with existing systems
- Develop and maintain full-stack applications using Ruby on Rails and Node.js
- Collaborate with cross-functional teams to define and implement new features
- Optimize application performance and scalability
- Ensure code quality through testing and code reviews
- Stay updated on new technologies and industry trends
|
Qualifications
|
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
- Proven experience in AI/ML development and implementation
- Deep understanding of blockchain technology and its applications
- Experience in full-stack development, including both front-end and back-end technologies
- Knowledge of version control systems (e.g., Git)
- Strong problem-solving and analytical skills
- Excellent communication and teamwork abilities
Preferred Qualifications / Skills
- Proficiency in Ruby on Rails and Node.js
- Self-motivated with a track record of seeing projects through to completion
- Demonstrated mastery of software engineering techniques
- Ability to work independently as well as part of a team
- Willingness to learn and adapt to new technologies
|
Key skills and knowledge for AI engineers
Programming:
Proficiency in Python is essential for AI development, particularly with libraries like TensorFlow and PyTorch. Knowledge of C++ or Java can broaden development capabilities.
Machine learning & deep learning:
AI engineers must understand machine learning concepts—supervised, unsupervised, and reinforcement learning—and deep learning, which allows automated analysis of large datasets.
Databases:
Experience with both relational (SQL) and non-relational (e.g., MongoDB) databases is important for managing and analyzing big data efficiently.
Mathematics:
Foundational math skills, including statistics, linear algebra, and calculus, are crucial for understanding AI algorithms, analyzing data, and optimizing model performance.
Useful certifications for AI Engineers
While no certification is strictly required for AI engineers, earning one can help demonstrate your skills and advance your career. Here are six valuable certifications:
G Test (Generalist Certification)
Offered by the Japan Deep Learning Association (JDLA), this exam provides a structured introduction to AI and deep learning, covering their applications and potential. The passing rate is around 70%, and there are no prerequisites. Passing grants a certificate, an open badge, and access to CDLE, Japan’s largest AI professional community.
E Test (Engineer Certification)
Also offered by JDLA, this certifies the ability to understand and implement deep learning. It tests knowledge of machine learning, deep learning, math, and development environments. Applicants must have completed a JDLA-certified program within the past two years.
Python 3 Engineer Certification
Provided by the Python Engineer Promotion Association, this certification covers Python fundamentals and practical applications, including data analysis. It has four levels: basic, practical, data analysis, and practical data analysis. No prerequisites are required.
Professional Data Engineer (Google Cloud)
This certification validates skills in cloud-based data analysis, system design, workload management, and automation. While there are no strict prerequisites, three years of practical experience is recommended. It is especially useful for projects using Google Cloud Platform (GCP).
AI-900 (Microsoft Azure AI Fundamentals)
Offered by Microsoft, this entry-level certification focuses on AI fundamentals and using Azure AI tools. Microsoft Azure is a leading cloud platform, and AI-900 serves as a gateway to intermediate and advanced Azure certifications.
AWS Certified Machine Learning – Specialty (MLS-C01)
Provided by Amazon Web Services, this certification covers the end-to-end process of machine learning on AWS, including data collection, processing, model building, and deployment. It is designed for engineers with at least two years of AWS machine learning experience and is highly valuable for AI development on AWS.
How to become an AI engineer with no experience
To become an AI engineer from scratch, it’s important to gradually build foundational knowledge and skills.
Study at a university or technical school
You can pursue AI engineering through programs in computer science, data science, or AI-specific courses. These institutions provide structured learning, guidance from experts, and practical experience through labs or research projects. Some employers value graduation from such programs, which can improve job prospects.
Learn online
Online platforms like Coursera, Udemy, and YouTube offer accessible AI and machine learning courses. Many free options are available, allowing beginners to explore AI before committing to more in-depth study.
Build experience through projects
Independent learners can strengthen their resumes by participating in projects or competitions. Platforms like Kaggle and Signate offer practical, hands-on experience. Once you gain skills, creating your own AI application can further demonstrate your abilities.
Characteristics of people suited for AI engineering
To thrive as an AI engineer, certain personal qualities are just as important as technical skills.
Problem-solving ability
AI engineers often propose solutions to business problems, not just implement client requests. They must identify the core issue, select appropriate data, process large datasets efficiently, and choose the right algorithms. Patience and flexible thinking are essential.
Love of continuous learning
AI technology evolves rapidly, with new algorithms and tools emerging constantly. AI engineers should be committed to ongoing learning through reading papers, joining communities, and staying updated on trends.
FAQ
What is the difference between IT engineers and AI engineers?
Both use IT to solve business problems, but IT engineers focus mainly on system development, infrastructure, and operations, following client requirements. AI engineers primarily build data analysis and machine learning models, and often advise clients on AI-based solutions.
Why do some people say “don’t become an AI engineer”?
AI engineering requires advanced, specialized skills and continuous learning, which can take time. However, the field is in high demand, offers salaries above ¥10 million, and provides opportunities to work on cutting-edge global projects.
Conclusion
Submit your resume
AI engineers play a crucial role in today’s technology landscape, designing and developing AI models, analyzing data, and driving innovation across industries. With strong demand, competitive salaries, and opportunities for career growth, pursuing a career as an AI engineer can be both rewarding and future-proof.
Interested in an AI or tech career? Robert Half specializes in connecting IT professionals with global and Japanese multinational companies. Submit your resume today and take the next step toward an exciting new career.
