What is an AI Team?
An AI team is a multidisciplinary group that combines diverse expertise to develop, deploy, and manage AI-driven solutions. The team is composed of various roles, each contributing unique expertise to achieve a common goal. The core roles within an AI team typically include:
- Product Managers — They oversee the product lifecycle, ensuring that AI capabilities are effectively integrated to meet business goals.
- AI Engineers — They are responsible for integrating AI models into software products and may also develop the APIs through which these models are accessed.
- Data Engineers — They manage data workflows, ensuring that data is properly collected, stored, and made accessible for fine-tuning.
- Domain Experts — They provide valuable insights into specific areas of application, ensuring that AI solutions are relevant and effective.
Additional roles that may complement an AI team include AI product managers, who lead teams on AI-related product initiatives, and AI engineers, who have a sound understanding of programming, software engineering, and data science. AI teams may also include roles such as data stewards, AI designers, and executives who provide strategic direction. Some organizations, especially AI research labs, have additional highly-specialized roles:
- Data Scientists — They analyze data, build machine learning (ML) models, and draw insights to improve existing models.
- Machine Learning Engineers — They create, develop, and test ML models, and build AI applications using embedded code or APIs.
- AI Architects — They focus on the transformational architecture efforts that AI introduces, orchestrating the deployment and management of models in production.
- AI Researchers — They conduct research to develop new AI algorithms and models, often working on cutting-edge aspects of AI.
- AI Ethicists and Sociologists — They ensure that AI solutions are developed and deployed in an ethical and socially responsible manner.
The success of an AI team depends on the effective collaboration of these roles, with each contributing their specialized knowledge to the development and implementation of AI solutions. As AI continues to evolve, the specific roles and responsibilities within AI teams may also adapt to new technologies and methodologies.
What is the difference between an AI team and a software engineering team?
An AI team and a software engineering team differ in their focus, methodologies, outcomes, and the tasks they set out to achieve.
Focus and Outcomes
AI teams focus on creating intelligent systems that can perform tasks similar to those performed by humans. They develop algorithms and models that enable machines to learn, reason, perceive, and make decisions based on data. The outcomes of AI are systems that can learn from data and improve over time.
On the other hand, software engineering teams focus on building software systems that meet specific user requirements. They are involved in the entire software development lifecycle, including requirements analysis, design, coding, testing, deployment, and maintenance. The outcomes of software engineering are reliable, efficient, and scalable software solutions.
AI teams use methodologies that involve observing user actions and system events, capturing data for analysis, predicting outcomes, and recommending specific actions. This process is iterative, with the system continuously refining its recommendations based on the latest feedback.
Software engineering teams, however, follow established software development methodologies. These methodologies guide the process of designing, building, testing, and maintaining software applications.
AI engineers design and craft AI models tailored to tackle diverse business challenges using machine learning techniques. They are responsible for optimizing these models' performance, monitoring their impact on real-world applications, and iterating upon them to continuously improve their effectiveness.
Software engineers, on the other hand, perform tasks such as designing, building, testing, and maintaining software applications across diverse industries. Their tasks encompass a wide array of skills and practices, including requirements analysis, design, coding, testing, debugging, security, documentation, and ongoing maintenance.
While AI and software engineering teams have distinct focuses and objectives, there is significant overlap between the two fields. Many AI systems are built using software engineering principles, and software engineers may incorporate AI techniques into their solutions. The two fields can complement each other, and professionals with expertise in both AI and software engineering have a broader skill set. Many teams are starting to merge, with an AI team being a complete AI and software engineering team.
It's important to note that the field of AI is still evolving, so these distinctions are not completely specified and different companies may use different job titles.