How to Transition into AI Engineering

AI is in demand.

You know this.

That is why you clicked this article.

It offers high salaries, exciting projects, and the opportunity to work on cutting-edge technology.

Companies worldwide—including SHIFT in Japan—are actively hiring AI engineers to develop generative AI applications, machine learning models, and automation solutions.

But what does it take to transition into AI engineering, especially if you’re coming from a different tech background?

Let's look at the 5 steps to transition in AI.

Why AI Engineering?

AI is transforming industries—from healthcare and finance to e-commerce and cybersecurity. AI engineers are in high demand because businesses need experts who can build and deploy AI-powered solutions. Some key reasons to move into AI include:

?? High earning potential – AI engineers often earn 30-50% more than traditional software engineers.

?? Future-proof career – AI is shaping the future, and the demand for AI talent will only grow.

?? Exciting challenges – Work on machine learning, generative AI models (like GPT-4), and deep learning applications.

?? Wide range of applications – AI engineers work in everything from robotics and automation to fraud detection and AI-powered customer service.

Now, let’s dive into how you can transition into AI engineering, step by step.

Step 1: Master the Fundamentals of AI & Machine Learning

Before you can start working as an AI engineer, you need a strong foundation in AI principles and machine learning (ML). If you have a software engineering background, you already have a head start, but even non-programmers can break into AI with the right approach.

Key Concepts to Learn:

?? Mathematics & Statistics – Linear algebra, probability, and calculus are essential for understanding how machine learning models work.

?? Machine Learning Basics – Supervised/unsupervised learning, regression, decision trees, and neural networks.

?? Deep Learning & Neural Networks – Study frameworks like TensorFlow and PyTorch.

?? Natural Language Processing (NLP) – Learn how AI can process text, including LLMs (Large Language Models) like GPT-4.

Best Learning Resources:

?? Online Courses:

• Andrew Ng’s Machine Learning (Coursera)
• Deep Learning Specialization (Coursera)
• Fast.ai Practical Deep Learning

?? Books:

• "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" – Aurélien Géron
• "Deep Learning" – Ian Goodfellow

Step 2: Learn AI Development Tools & Frameworks

AI engineers don’t just understand theory—they build real applications. To do this, you need to get comfortable with the most widely used AI tools and frameworks.

Essential AI Technologies to Learn:

?? Programming Languages: Python (NumPy, Pandas, Scikit-Learn), R (for data analysis), SQL

??? Machine Learning Frameworks: TensorFlow, PyTorch

?? Data Processing Tools: Apache Spark, Dask

?? Cloud AI Platforms: AWS SageMaker, Azure AI, Google Cloud AI

SHIFT’s AI Engineers Use:

At SHIFT, AI engineers work with a wide range of technologies to build and deploy AI-powered products:

? Backend: Python, Go, TypeScript

? Cloud Platforms: AWS, Azure, GCP

? AI/ML Models: GPT-4, OpenAI API, LangChain

Building projects?using these frameworks is critical if you want to work on real AI applications.

Step 3: Gain Practical Experience with AI Projects

To stand out as an AI engineer, build real AI projects and showcase them in your portfolio.

Beginner AI Projects:

??? Image Recognition System – Train a model to classify images (cats vs. dogs, etc.).

?? Stock Price Prediction – Use ML models to predict stock prices.

?? Chatbot Development – Create a chatbot using GPT-4 or Azure OpenAI.

Advanced AI Projects:

?? Generative AI Applications – Build tools like SHIFT’s Tensai-kun, which allows users to create AI-powered workflows.

?? AI Document Reverse Engineering – Train AI models to read and automate documentation from codebases.

?? AI-Powered Search Engines – Implement RAG (Retrieval-Augmented Generation) to improve search accuracy in AI chat applications.

Step 4: Transition Your Career into AI Engineering

If you’re coming from software development, data science, or another tech role, the transition into AI is smoother. Here’s how to position yourself for AI job opportunities:

For Software Engineers:

• Shift from traditional backend/frontend work to AI model development.
• Learn about ML model deployment and AI APIs (e.g., OpenAI, LangChain, Hugging Face).

For Data Scientists:

• Gain more experience in deep learning and generative AI.
• Learn MLOps (Machine Learning Operations) to automate AI pipelines.

For Non-Tech Professionals:

• Start with data analysis and AI basics.
• Gain hands-on experience with low-code AI tools like AutoML and SHIFT’s AI solutions.

Step 5: Apply for AI Engineering Jobs (Where to Look)

Once you’ve built a solid foundation, it’s time to apply for AI jobs. SHIFT is currently hiring AI professionals in Japan!

AI Careers at SHIFT:

?? AI Engineer – Work on generative AI models, machine learning, and business automation tools.

?? AI Consultant – Help companies integrate AI into their business operations.

?? AI Pre-Sales Specialist – Design AI solutions that meet client needs.

SHIFT is leading the AI transformation in Japan, developing cutting-edge AI tools like Tensai-kun (No-Prompt AI Platform) and AI Document Reverse Engineering.