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# AI Engineer vs ML Engineer
An AI Engineer and an ML Engineer have overlapping roles but distinct areas of focus within the AI ecosystem. An AI Engineer typically works on building and integrating comprehensive AI solutions, which can include natural language processing (NLP), computer vision, and intelligent automation systems, utilizing a combination of machine learning, deep learning, and rule-based AI techniques. They focus on creating end-to-end systems that can simulate human-like capabilities. In contrast, an ML Engineer specializes more narrowly on designing, training, and optimizing machine learning models, ensuring they perform efficiently and are integrated into production environments. While both roles require a deep understanding of algorithms and data, AI Engineers work on broader applications, combining various AI technologies, whereas ML Engineers are more concerned with the underlying models and their scalability.
An AI Engineer uses pre-trained models and existing AI tools to improve user experiences. They focus on applying AI in practical ways, without building models from scratch. This is different from AI Researchers and ML Engineers, who focus more on creating new models or developing AI theory.
Learn more from the following resources:

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Learn more from the following resources:
- [@official@GitHub Copilot](https://github.com/features/copilot)
- [@official@Codeium](https://codeium.com/)
- [@official@Supermaven](https://supermaven.com/)
- [@official@Tabnine](https://www.tabnine.com/)

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AI has given rise to a collection of AI powered development tools of various different varieties. We have IDEs like Cursor that has AI baked into it, live context capturing tools such as Pieces and a variety of brower based tools like V0, Claude and more.
- [@official@Pieces Website](https://pieces.app)
- [@official@v0 Website](https://v0.dev)
- [@official@Claude Website](https://claude.ai)
- [@official@Aider - AI Pair Programming in Terminal](https://github.com/Aider-AI/aider)
- [@official@Replit AI](https://replit.com/ai)
- [@official@Pieces Website](https://pieces.app)

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# Introduction
AI Engineering is the field focused on designing, building, and deploying AI systems to solve real-world problems. It merges software engineering, data science, and machine learning to create scalable and efficient solutions. AI Engineers handle tasks from data preparation and model training to integrating AI into software applications, ensuring these systems perform reliably in production. Covering areas like natural language processing, computer vision, and robotics, AI Engineering bridges the gap between research and real-world application, driving innovation across industries such as healthcare, finance, and autonomous systems.
AI Engineering is the process of designing and implementing AI systems using pre-trained models and existing AI tools to solve practical problems. AI Engineers focus on applying AI in real-world scenarios, improving user experiences, and automating tasks, without developing new models from scratch. They work to ensure AI systems are efficient, scalable, and can be seamlessly integrated into business applications, distinguishing their role from AI Researchers and ML Engineers, who concentrate more on creating new models or advancing AI theory.

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- [@article@How to Become an AI Engineer: Duties, Skills, and Salary](https://www.simplilearn.com/tutorials/artificial-intelligence-tutorial/how-to-become-an-ai-engineer)
- [@article@AI engineers: What they do and how to become one](https://www.techtarget.com/whatis/feature/How-to-become-an-artificial-intelligence-engineer)
- [@course@AI For Everyone](https://www.coursera.org/learn/ai-for-everyone)
- [@video@AI Engineers- What Do They Do?](https://www.youtube.com/watch?v=y8qRq9PMCh8&t=1s)

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