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 – allowing companies to remain agile, increase productivity, and fuel insights. But to achieve that and set your organization up for AI success, you need a solid team of technologists, data scientists, and product specialists as the foundation.
Whether you’re building a team from scratch or growing an existing AI team – or simply want to improve workflows and cross-functional collaboration – this practical guide will break down some of the key components needed to bring together the right team.
Of course, every business is different and might have contrasting needs. However, several critical roles shape a well-rounded, successful AI team. Here are the core roles you should consider hiring.
1. Engineers: From idea to production
First, you need a machine learning (ML) engineer or researcher to build the models based on a given data set and the problem you’re looking to solve. If it’s a well-understood task, you can opt for an ML engineer. If it’s a task no one has ever solved, then you’ll likely need an ML researcher.
From there, the next crucial hire is an infrastructure engineer who creates and executes the supporting functions and backend infrastructure needed for the ML engineers to make AI models work. For example, when you want to build an AI model, you often need to scale your training and evaluation to run quickly by leveraging various cloud instances. The infrastructure engineer makes it easier for ML engineers to iterate the loop of developing, training, and evaluating models.
You also need engineers who can translate models from research into live production. This includes building APIs, handling errors, logging, and monitoring. Optimizing the cloud computing cost will also eventually come up if the product is successful. To account for this, engineers in this role will need to constantly monitor data set drift and set up retraining jobs to update the models on an ongoing basis.
2. Data scientists: From labeling to analyzing
Consider hiring a data expert who can create dashboards that allow the business teams to see and understand the overall metrics of your project easily.
Data scientists are also important to hire for AI teams. Often, you don’t need to create new models for certain problems; you can instead clean and analyze existing data. Data scientists can quickly slice and dice data using SQL and visualize it.
[ Related read Data scientist: A day in the life ]
For many ML tasks, you need an interface that enables data labelers to work quickly and accurately. Therefore, you’ll need a developer to build a native or web interface. If you hire data labelers, you also need a QA engineer to track and review their work to ensure quality.
Additionally, consider hiring a data expert who can create dashboards that allow the business teams to see and understand the overall metrics of your project easily. This role could be a business data analyst or data scientist. Having this role on your team ensures the rest of the organization – specifically non-technical people – get visibility into the great results your team is achieving.
3. Product managers: From technical know-how to market solutions
Finally, you need a product manager who understands how to map out and leverage the strengths and weaknesses of AI. For instance, models for classification can output a score for how likely an example is to 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 figure out how to design a great user experience in the face of such uncertainty. For example, they might find that a search engine is a good solution because even when the top answer is wrong, there can be value in the second and third answer being right. The person in this role will ensure your product is designed around the strengths and limitations of your ML models.
Bring your AI project to the next level
By hiring an interdisciplinary team where collaboration is encouraged and embraced, you’ll likely see dividends pay off in the form of employee satisfaction and the ability to build and scale end-to-end AI solutions that drive 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’re focusing on your company’s overall success.
[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]