Towards Data Science is a community-powered publication that showcases work in data science, machine learning and artificial intelligence. Every day newcomers, seasoned researchers and industry practitioners publish tutorials, research notes and real-world case studies that help the field move forward.

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AI engineers specialize in integrating and deploying foundational GenAI models like GPT and Claude into products, focusing on rapid deployment and product development. Machine learning engineers build custom models from scratch using traditional algorithms like gradient boosted trees and neural networks, requiring deeper mathematical knowledge in statistics, linear algebra, and calculus. Both roles share foundational software engineering skills but differ in domain expertise: AI engineers need GenAI/LLM knowledge while ML engineers require advanced algorithmic understanding. AI engineering has a lower barrier to entry and growing demand, while ML engineering is more established with higher median salaries. Career choice depends on preference for mathematical depth versus rapid product development.

Machine Learning vs AI Engineer: What Are the Differences?