In almost every industry, artificial intelligence (AI) is no longer a “nice-to-have” technology, but a mission-critical solution to address urgent business needs – enabling organizations to stay agile, increase productivity and uncover insights. But to do that and position your business for AI success, you need a solid team of technologists, data scientists, and product specialists as a foundation.
Whether you’re building a team from scratch, expanding an existing AI team—or just looking to improve workflows and cross-functional collaboration—this practical guide explains some of the key components needed to bring the right team together.
Of course, every business is different and may have different requirements. However, several critical roles shape a well-rounded, successful AI team. Here are the core roles to consider.
1. Engineers: From idea to production
First, you need a machine learning (ML) engineer or researcher to create the models based on a specific data set and the problem to be solved. If it is a well-understood task, you can go for an ML engineer. If it’s a problem no one has ever solved, you probably need an ML researcher.
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From there, the next key hire is an infrastructure engineer, who will build and run the supporting functions and backend infrastructure that the ML engineers need to make AI models work. For example, if you want to build an AI model, you often need to scale your training and assessment to run quickly by leveraging different cloud instances. The infrastructure engineer makes it easy for ML engineers to go through the loop of developing, training, and evaluating models.
They also need engineers who can translate models from research into live production. This includes building APIs, handling errors, logging, and monitoring. Cloud computing cost optimization will eventually come if the product is successful. To accommodate this, engineers in this role must constantly monitor data set drift and set up retraining jobs to continually update the models.
2. Data Scientists: From labeling to analysis
Consider hiring a data expert who can create dashboards that allow the business teams to easily see and understand the overall metrics of your project.
Data scientists are also important for AI teams to hire. Often you don’t need to create new models for specific problems; Instead, you can clean and analyze existing data. Data scientists can quickly decompose and visualize data using SQL.
[ Related read Data scientist: A day in the life ]
For many ML tasks, you need an interface that allows data labelers to work quickly and accurately. Therefore, you need a developer to create a native or web interface. When you hire data labelers, you also need a QA engineer to track and review their work to ensure quality.
Also, consider hiring a data expert who can create dashboards that allow the business teams to easily see and understand the overall metrics of your project. This role could be a Business Data Analyst or Data Scientist. This role on your team will ensure that the rest of the organization – especially non-technical people – has visibility into your team’s great results.
3. Product Manager: From technical know-how to market solution
Finally, you need a product manager who understands how to identify and leverage the strengths and weaknesses of AI. For example, classification models can give a score for how likely it is that an example will fall into the positive class. The higher the score, the more confident the model is that the example is positive.
A product manager can help you design a great user experience in the face of such uncertainties. For example, they might find that a search engine is a good solution because even if the top answer is wrong, there can be value in the second and third answers being right. The person in this role ensures that your product is based on the strengths and limitations of your ML models.
Take your AI project to the next level
When you hire an interdisciplinary team where collaboration is encouraged and embraced, you’ll likely find the dividends reap the rewards in employee satisfaction and the ability to design and scale end-to-end AI solutions that meet the Increase business value efficiently.
Your AI product is only as strong as your team – and by hiring strategically and focusing on your team’s success, you focus on the overall success of your business.
[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]