AI engineer vs. data scientist: What's the difference?

What does an AI engineer do?

As a formal job title, AI engineering is relatively new compared with data science, although much of the work itself -- such as deploying ML models and scaling AI applications -- has been around for years under various names. Whereas data science has clear education paths and established industry standards, AI engineering is still evolving, and different companies define the role in different ways.

This can have benefits and disadvantages. On the one hand, the "AI engineer" title is definitely trendy at the moment, which usually means high demand and a nice salary bump. But it can also create confusion if employers and job applicants aren't clear on what an AI engineer at a given company will actually do.

As more companies begin to use AI, this role will likely become better defined, with more consistent skill expectations and job descriptions. For now, it's safe to say that most organizations seeking an AI engineer want someone with a strong software engineering and ML background, including knowledge of model deployment best practices and DevOps principles.

At its core, AI engineering is about making ML models work in the real world. By deploying AI systems to production, AI engineers transform models into fully realized applications that people can actually use. That also means maintaining those applications to make sure that they're reliable, scalable and well-integrated with the rest of the organization's IT environment.

Unlike data scientists, AI engineers don't usually focus on exploring raw data to find trends, patterns or relationships. Although they might interact with raw data in certain cases, such as analyzing training data to debug model performance, they're more likely to take an already-functioning model and ensure it works well in production. That might involve, for example, optimizing algorithms to reduce latency or choosing a deployment infrastructure that balances performance and cost efficiency.

Within those broad constraints, though, there's variance from business to business. Some AI engineers build APIs and manage cloud infrastructure for serving prebuilt models, whereas others work on optimizing models themselves or integrating them into CI/CD pipelines.

Key tools and skills for AI engineers include the following:

• Knowledge of software development, CI/CD and DevOps principles.
• Machine learning frameworks and libraries, such as TensorFlow and PyTorch.
• Cloud platforms, such as AWS, Google Cloud and Microsoft Azure.
• DevOps tools, such as container orchestrator Kubernetes and infrastructure as code platform Terraform.
• Programming languages commonly used in machine learning, such as Python, C++ and Java.

What does a data scientist do?

The data scientist role evolved from a long history of jobs involving analyzing and managing information. Data scientists are responsible for gathering and preparing messy real-world data sets, then understanding them through a combination of statistical methods, ML algorithms and domain knowledge.

Most data scientists' workflows involve collecting and cleaning data; developing and training models; and creating dashboards, presentations and reports for other teams, including nontechnical business stakeholders. Whereas AI engineers are usually working with models that already exist -- whether an off-the-shelf LLM from providers like OpenAI and Anthropic, or a predictive model built in-house -- data scientists collect the training data and build the actual models.

Like AI engineers, data scientists need a foundation in computer programming and machine learning, especially Python and ML frameworks like PyTorch and TensorFlow. However, data science's focus on exploration, model building and communicating findings means that they use some tools and skills that AI engineers don't:

• Coding languages commonly used in statistics and data analysis, such as R and SQL.
• Data-focused Python libraries, such as pandas, NumPy and scikit-learn.
• Data visualization and reporting tools, such as the Jupyter Notebook IDE, Python libraries like Matplotlib and Seaborn, and business intelligence tools like Tableau and PowerBI.
• Statistics software such as Stata, Matlab and SPSS, if working in an academic or research environment.

AI engineer vs. data scientist: Major similarities and differences

AI engineers and data scientists might have different day-to-day tasks, but they share a common foundation:

• Analytical thinking and problem-solving. Both AI engineers and data scientists break down complex problems and design efficient solutions, whether optimizing an application that relies on neural networks or analyzing large data sets to identify business trends.
• Programming proficiency. While the specific languages differ, both AI engineers and data scientists need a strong coding foundation, typically involving extensive Python.
• Machine learning fundamentals. Both roles need to understand, at minimum, how machine learning models work, how to tune hyperparameters and how to evaluate model performance.

However, the two roles also differ in some important ways:

• Scope of work. AI engineers integrate AI models into scalable, efficient systems that serve users in real-time applications. In contrast, data scientists handle the exploratory and interpretive aspects of model development: extracting meaning from historical data, designing and refining models, and producing insights to support business decision-makers.
• Area of technical expertise. In general, AI engineers should be comfortable with application deployment, cloud computing, and infrastructure management and scaling. Data scientists focus more on data cleaning and exploration, statistical analysis, and hypothesis testing. In addition, AI engineers often work with lower-level languages, such as C++ or Java, whereas data scientists are more likely to use R or SQL.
• Organizational role. AI engineers usually work closely with software developers, IT operations and product teams to build AI-powered applications. Data scientists might also work with these teams, but to a lesser extent; they're more likely to collaborate with business stakeholders like operations analysts and line-of-business professionals.

Examples of AI engineering and data science in practice

To illustrate the difference between the two roles, imagine an automotive company developing an AI-assisted driving system to improve navigation and obstacle detection.

The first step is to develop an accurate ML model that can identify objects, pedestrians and other vehicles. Data scientists start this process by analyzing sensor data, such as camera footage, lidar and radar, sometimes working with data engineers to handle initial data collection and preprocessing. They then experiment with algorithmic architectures, tune model hyperparameters, and use statistical analysis to identify unwanted behaviors -- for example, failure to perform correctly in snowy weather conditions.

Once the model is developed, the next step is for AI engineers to optimize and deploy it, paying attention to real-world performance considerations. They also help integrate the AI components into the vehicle's broader software system. For example, knowing that the model must be able to respond quickly in real time, AI engineers might aim to reduce latency at inference time using techniques like model compression and quantization to ensure that the AI system can run on an edge device within the vehicle.

For another example, take an AI recommendation system at an online retailer. A data scientist starts by collecting and analyzing purchasing data, then trains a machine learning model to predict what consumers are likely to buy next based on those historical patterns. Next, an AI engineer steps in to make sure the system can handle real-time requests by optimizing it for performance, integrating it into the overall e-commerce platform and deploying it to production.

Lev Craig covers AI and machine learning as the site editor for SearchEnterpriseAI. Craig graduated from Harvard University with a bachelor's degree in English and has previously written about enterprise IT, software development and cybersecurity.