Business leaders are excited by the possibilities of AI. However, hiring or assigning the right talent for AI projects can be challenging. What role do the team actually need?
AI is a broad and comprehensive term that includes generation AI such as CHATGPT and other large-scale language models (LLMS), and is a traditional machine learning format (ML), such as predictive analytics and recommended systems. This means that the composition of an AI team may vary widely from project to project, depending on the scope and technical skills required.
The two general roles of the AI project team are: AI Engineer and Data Scientist. Both are involved in developing AI systems and applications, but job details are different.
- Data scientists collect and clean data and use statistics and machine learning to derive insights from it. They are responsible for understanding what data actually means and using that knowledge to predict future outcomes and inform decision-making.
- AI engineers build and maintain systems that integrate AI and machine learning models into real-world applications. The AI Engineer duties may be very similar to the MLOPS or DevOps Engineer duties of other companies, but some organizations distinguish ML Engineers from other roles.
What do AI engineers do?
As a formal position, AI engineering is relatively new compared to data science, but much of the tasks themselves, such as deploying ML models and scaling AI applications, have existed for many years under a variety of names. Although data science has clear educational paths and established industry standards, AI engineering is still evolving, with different companies defining roles differently.
This has its advantages and disadvantages. On the other hand, the “AI Engineer” title is definitely popular at this point. This usually means high demand and great pay bumps. However, it can cause confusion if employers and job seekers aren't clear about what AI engineers in a particular company actually do.
As more companies begin to use AI, this role may be better defined by more consistent skill expectations and job descriptions. For now, it's safe to say that most organizations looking for AI engineers want someone with a strong software engineering and ML background, including knowledge of model deployment best practices and DevOps principles.
AI engineering is at the heart of it, making ML models work in the real world. By deploying AI systems in production, AI engineers transform the model into fully realized applications that can be used in practice. It also means maintaining these applications to ensure that they are reliable, scalable and integrated with the rest of your organization's IT environment.
Unlike data scientists, AI engineers usually do not focus on exploring raw data to find trends, patterns, or relationships. Although it may interact with raw data in certain cases, such as analyzing training data to debug the performance of a model, it is likely to employ models that are already working and work well in production. This includes, for example, optimizing algorithms to reduce latency or choosing a deployment infrastructure that balances performance and cost-effectiveness.
However, among these broad constraints, there are differences between businesses. Some AI engineers are working to optimize the model itself or integrate it into a CI/CD pipeline, whereas some AI engineers manage the cloud infrastructure to build APIs and deliver pre-built models.
Key tools and skills for AI engineers include:
- 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, Microsoft Azure.
- Infrastructure as DevOps tools such as container orchestrator Kubernetes and code platform Terraform.
- Programming Language It is commonly used in machine learning such as Python, C++, Java.
What do data scientists do?
The role of data scientists has evolved from a long history of work, including information analysis and management. Data scientists are responsible for collecting and preparing troubling real-world datasets and understanding them through a combination of statistical methods, ML algorithms, and domain knowledge.
Most data scientists' workflows include data collection and cleaning. Development and training models. Create dashboards, presentations and reports for other teams, including non-technical business stakeholders. On the other hand, AI engineers usually deal with models that already exist – whether off-the-shelf LLMs from providers like Openai or humanity, or in-house built prediction models, data scientists collect training data and build real models.
Like AI engineers, data scientists need the foundations of computer programming and machine learning, particularly ML frameworks such as Python, Pytorch and Tensorflow. However, data science's focus on exploration, model building and communication outcomes means using tools and skills without AI engineers.
- A coding language commonly used in statistics and data analysis such as R and SQL.
- Data-centric Python libraries such as Pandas, Numpy, and Scikit-Learn.
- Data visualization and reporting tools such as Jupyter notebook IDE, Python libraries such as Matplotlib and Seaborn, and business intelligence tools such as Tableau and Powerbi.
- Statistical Software When working in an academic or research environment, you can use STATA, MATLAB, SPSS, etc.
AI Engineers vs Data Scientists: Key Similarities and Differences
AI engineers and data scientists share a common foundation, although their daily tasks may differ.
- Analytical thinking and problem solving. Both AI engineers and data scientists analyze complex problems and design efficient solutions whether they optimize applications that rely on neural networks or analyse large datasets to identify business trends.
- Programming proficiency. Although certain languages vary, both AI engineers and data scientists require a strong coding foundation, usually including extensive Python.
- The fundamentals of machine learning. Both roles require at least an understanding of how machine learning models work, how to tune hyperparameters, and how to evaluate model performance.
However, the two roles also differ in several important ways.
- The scope of work. AI engineers integrate AI models into scalable and efficient systems that serve users in real-time applications. In contrast, data scientists handle exploratory and interpretive aspects of model development. Extract meaning from historical data, design and refine models, and create insights to support business decision makers.
- A field of technical expertise. In general, AI engineers should be satisfied with application deployment, cloud computing, infrastructure management and scaling. Data scientists focus more on data cleaning and exploration, statistical analysis, and hypothesis testing. Furthermore, AI engineers often use lower-level languages like C++ or Java, while data scientists are more likely to use R or SQL.
- Organisational roles. AI engineers typically work closely with software developers, IT operations and product teams to build AI-powered applications. Data scientists may also work with these teams, but not so much. They are more likely to work with business stakeholders, such as operations analysts and business line experts.
Examples of real AI engineering and data science
To explain the difference between the two roles, imagine an automotive company developing AI-assisted driving systems 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 begin this process by analyzing sensor data such as camera footage, LIDAR, and radar, and work with data engineers to handle initial data collection and preprocessing. Next, we experiment with the algorithm architecture, adjust the model hyperparameters, and use statistical analysis to identify unnecessary behaviors.
Once a model is developed, the next step is for AI engineers to optimize and deploy it. Pay attention to actual performance considerations. It also helps to integrate AI components into the vehicle's wider software system. For example, knowing that a model needs to be able to respond quickly in real time, AI engineers aim to enable AI systems to run on edge devices within the vehicle using techniques such as model compression and quantization.
For another example, take AI recommendation systems at online retailers. Data scientists start by collecting and analyzing purchase data, then train machine learning models to predict what consumers may purchase next, based on these historical patterns. The AI engineer then ensures that the system can handle real-time requests by optimizing for performance, integrating it into the e-commerce-wide platform, and deploying it into production.
Lev Craig covers AI and machine learning as the site editor for SearchenterPriseai. Craig graduated from Harvard University with a Bachelor of Arts in English and previously wrote about Enterprise IT, software development and cybersecurity.
