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Day in the life as a AI Engineer?
Hello, I am an aspiring computer science student and am applying to CS programs for the goal of becoming an AI engineer. However, while there are various videos out there stating the day in a life as a software engineer among many other positions, I note that there hasn't really been any as an AI engineer, so thoughts?
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Sandeep’s Answer
Hello Michelle,
An AI Engineer’s day is less about a fixed routine and more about a scientific and iterative process. Your primary focus is on building a system that learn from data and continue improve over time. A large portion of your morning is often spent managing the data lifecycle: collecting, cleaning, and transforming datasets. You will also check the results of overnight model training experiments, analyze the performance metrics (like accuracy and loss), and engage in team meetings to discuss architectural decisions and new feature requirements.
The bulk of your day is spent experimenting, coding, and deploying. This means heavy use of Python and specialized frameworks like PyTorch or TensorFlow to build and fine-tune models.
An AI Engineer’s day is less about a fixed routine and more about a scientific and iterative process. Your primary focus is on building a system that learn from data and continue improve over time. A large portion of your morning is often spent managing the data lifecycle: collecting, cleaning, and transforming datasets. You will also check the results of overnight model training experiments, analyze the performance metrics (like accuracy and loss), and engage in team meetings to discuss architectural decisions and new feature requirements.
The bulk of your day is spent experimenting, coding, and deploying. This means heavy use of Python and specialized frameworks like PyTorch or TensorFlow to build and fine-tune models.
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Hastha’s Answer
Michelle, I've spent the last 10 years creating AI and data science tools across different industries. My key advice for you is to "fall in love with the problem." As engineers, we often focus on the technology, but it's crucial to truly understand the problem, its scope, and who it affects. This way, the solutions we build can genuinely make a difference.
Connect with AI engineers on LinkedIn and ask for an informational interview / discussion for 20-30m to understand their 'jobs to be done'
Hastha recommends the following next steps:
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David’s Answer
Since you are an aspiring CS student, you are in a great position to build the skills necessary for an AI Engineer role. This career requires a strong foundation in both software engineering and machine learning. You should focus on mastering programming languages like Python, understanding core computer science concepts such as data structures and algorithms, and developing a solid grasp of probability, statistics, and linear algebra, which are the mathematical backbone of AI models. Additionally, gaining practical experience with machine learning frameworks (like TensorFlow or PyTorch) and working on projects involving data preprocessing and model deployment will be crucial for your success.
While in your CS program, look for opportunities to specialize in Machine Learning Engineering, which is closely related to the AI Engineer role. Focus on coursework that covers cloud computing (AWS, Azure, or GCP), building data pipelines, and MLOps—the practice of deploying and maintaining ML systems reliably. By consistently building projects, contributing to open-source initiatives, and seeking internships, you will bridge the gap between theoretical knowledge and the practical skills required to seamlessly integrate AI models into large-scale, real-world applications.
While in your CS program, look for opportunities to specialize in Machine Learning Engineering, which is closely related to the AI Engineer role. Focus on coursework that covers cloud computing (AWS, Azure, or GCP), building data pipelines, and MLOps—the practice of deploying and maintaining ML systems reliably. By consistently building projects, contributing to open-source initiatives, and seeking internships, you will bridge the gap between theoretical knowledge and the practical skills required to seamlessly integrate AI models into large-scale, real-world applications.