ML Engineer vs AI Engineer
Definition
ML Engineers focus on training and optimizing machine learning models from scratch, while AI Engineers specialize in building applications using pre-trained foundation models like GPT-4 or Claude.
Key Differences
ML Engineers work closer to the model layer: training custom models, feature engineering, model optimization, and working with structured data. They typically need strong statistics and mathematics backgrounds and often work with tools like PyTorch, TensorFlow, and Scikit-learn.
AI Engineers work at the application layer: integrating LLMs via APIs, building RAG pipelines, designing prompts, and implementing AI agents. They need strong software engineering skills and work with tools like LangChain, vector databases, and cloud APIs.
Which to Choose
Choose ML Engineering if you: enjoy mathematics and statistics, want to build models from scratch, prefer working with structured data, or want to specialize in computer vision or NLP model development.
Choose AI Engineering if you: prefer building products over research, want to leverage existing foundation models, enjoy rapid prototyping and iteration, or come from a software engineering background.
Market Reality
In 2025, AI Engineering roles are growing faster due to the explosion of LLM applications. However, ML Engineering remains essential for companies building custom models or working in specialized domains. Many professionals develop skills in both areas.