AI Engineer vs. Data Scientist: What is Your Best Fit?

Today's business leaders are very hopeful about the future of AI, but there is a lot of confusion when it comes to choosing the right talent for an AI project. Not every AI system is the same because some projects are based on generative AI, like ChatGPT, while some use predictive models, like traditional machine learning. This is why the company requires different tech professionals depending on the project.

The two roles that are important in almost every project are: a Data Scientist and an AI engineer. The job of a data scientist is to understand, clean the data, and extract insights from it. Whereas the AI ​​engineer deploys the models in real-world applications, ensuring the AI system works smoothly for the end users.

Both AI Engineer and Data Scientist are high-paying jobs. The average salary of AI Engineers is between $120,000 and $200,000, while Data Scientists earn over $150,000.

Glassdoor

In this blog, we will read about the roles, skills, and growth of AI Engineer and Data Scientist along with the courses to elevate your tech career.

What Does an AI Engineer Do?

These days the job title of “AI Engineer” is heard everywhere – from LinkedIn to hiring portals. But the real question is: what does an AI engineer do? While the role of a data scientist has been established for quite some time with clear education paths and defined responsibilities, AI engineering is a new and evolving field. Every company has a different definition to define this wonderful role.

Generally speaking, the main focus of AI engineers is to deploy machine learning (ML) models in production and transform them into real-world applications. The role is not just about building models but making those models scalable, reliable, and business-ready. They work with cloud platforms including AWS, Azure or Google Cloud, use DevOps tools (Kubernetes, Terraform). Further, they are proficient in programming languages ​​like Python, C++ and Java.

At some places, AI engineers build APIs, set up CI/CD pipelines in a project, and sometimes even do model optimization for better performance. Their work ranges from back-end integration to infrastructure setup.

The biggest benefit of this role? Demand is high, salaries are impressive, and one gets a chance to work in the most exciting zone of technology. But the challenge is that expectations vary. Therefore, to become an AI engineer, not only ML knowledge but also strong software engineering and DevOps understanding are necessary.

What Does a Data Scientist Do?

In today’s data-driven era, becoming a data scientist has become a prestigious and in-demand role. But people wonder – what does a data scientist do? This role is for people who clean up messy real-world data and extract meaningful insights from it. Data scientists collect data, clean it, and then develop models by applying statistical methods and machine learning algorithms.

While AI engineers mostly deploy pre-trained models in production, data scientists build models on their own, prepare training data, analyse them and communicate results to decision-makers via reports and dashboards.

Tools like Python, SQL, R, Pandas, NumPy, and Jupyter Notebook are part of their daily workflow. And for visualization they also use tools like Tableau, PowerBI. This role demands business understanding along with technology.

Brains Behind the Code: AI Engineer or Data Scientist?

Top AI Courses for Engineers & Data Science Certifications You Can Trust

The Certified Artificial Intelligence Engineer (CAIE™) program issues by the United States Artificial Intelligence Institute are recognized as the best AI Engineer Certification. This program offers self-paced e-learning modules, practice codes, and real-world applications to build foundational AI/ML skills. Furthermore, to scale your AI competencies to the specialists’ levels- the Certified Artificial Intelligence Scientist (CAIS™) program aims to offer deep knowledge in AI, including deep learning, computer vision, reinforcement learning, and much more.

Further, Certified Lead Data Scientist (CLDS™) is issued by the United States Data Science Institute are recognized as the top Data Science Certification for professionals with at least 2 years of experience and focuses on advanced analytics, big data, and leadership in data science projects. Moreover, if you wish to level up- Certified Senior Data Scientist (CSDS™) is for seasoned professionals with over 5 years of experience, emphasizing strategic decision-making and advanced data science methodologies.

Other recognized universities or institutions provide certification for AI Engineers and Data Scientists. For example, the University of Michigan—Applied Data Science with Python (Coursera) introduced Python for data analysis, ML, and visualization. Imperial College London offers an AI and Machine Learning Specialization (Coursera) that focuses on AI fundamentals and real-world AI solutions.

Conclusion

Both AI Engineering and Data Science are booming careers, but their focus is different. AI Engineers mainly work on real-time systems and deployment, while Data Scientists extract insights and support business decisions. We have read a detailed differentiation about both and observed that Programming, ML knowledge, and analytics skills are required for both roles. According to your interests and career goals, you can get a globally recognized certification in any of these fields.