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How to Build a Great AI Engineering Team

Michael Wharton
Vice President of Engineering
Larry Gray
Director of Engineering


Building a top-notch AI engineering team is crucial for success in today’s rapidly evolving AI industry.  To achieve this, three key concepts must be considered: culture, technical fluidity, and management style.


The foundation of a great culture is incorporating core values into all aspects of the business. At our company, we prioritize Inventive, Trustworthy, Caring, Open, and Inquisitive values. A team's success depends heavily on the strength of its culture. We prioritize people over code and promote psychological safety to create a supportive environment for our team. We also encourage work-life balance by offering flexible paid time off.

We prioritize hiring a diverse and inclusive team to establish a strong culture. This enhances creativity, innovation, and problem-solving skills and potentially reduces bias in ML models. When selecting candidates, we look for engineers who possess the "Humble, Hungry, and Smart" characteristics described by Patrick Lencioni. These individuals are humble enough to seek better solutions, eager to tackle complex problems, and socially savvy.

Our team culture is focused on contribution rather than being right or wrong. This fosters creativity, experimentation, and innovation while promoting collaboration, continuous improvement, and learning. To maintain this culture of innovation, we encourage it through functional projects such as the Horizon Group. This group is tasked with measuring innovation as an asset and provides engineers with access to an Innovation Lab where they can explore new AI ideas and bring them to life.

Technical fluidity

Given that KUNGFU.AI provides professional services, the nature of our clients’ respective environments and tool sets can vary dramatically. We work with clients fully invested in Amazon Web Services (AWS) or alternative cloud platforms, others with exclusively on-prem infrastructure aided by serverless components, and many others across a broad spectrum of infrastructure. Additionally, the field of artificial intelligence and machine learning moves so incredibly fast that we often utilize months-old research to solve a particular problem on a given day. How can a team optimize for this kind of work with such a moving target? In short, they can’t.

To address this conundrum, the engineering team at KUNGFU.AI indexes heavily on technical fluidity. Of course, we screen for a foundational technical skill set that includes software, data science, and machine learning fundamentals. Still, for the most part, a growth mindset is the single-most important asset we value in new team members. We also have a host of mechanisms to nurture this mindset internally, such as enabling conference attendance, hosting weekly professional development sessions with external guest speakers, and allowing for a dedicated “Lab Day” on Fridays. On Friday, team members can focus on anything and everything they deem vital to their growth.

Furthermore, the engineering team’s notion of “technical fluidity” includes soft skills. We recognize that perfect answers to hard questions are immaterial without the ability to communicate them thoughtfully. These soft skills provide a necessary complement to our class-leading technical ability that enables us to do what we claim to do, which is to offer state-of-the-art solutions to our clients. And hey, we have fun while we do it!

Management style

To build a great engineering team, it's crucial to have a management style that supports and empowers team members. This can be achieved through three key elements.

Firstly, it's essential to have experts leading experts. This means that team leaders should have a deep understanding of their team's technical challenges or at least have the capacity to learn these skills. They should be subject matter experts (SMEs) in their field.

Secondly, we believe in the concept of stewardship, where the manager's growth is directly tied to the growth of the team being managed. This means that managers must actively work to support their team members' development and success.

Thirdly, setting clear goals and priorities is essential. During our weekly one-on-one meetings, we ask questions to ensure that team members clearly understand the overarching objectives of their current projects and their role and responsibilities within the team. This helps ensure everyone is aligned and working towards the same goals.


In conclusion, building a great AI engineering team is a complex and ongoing process. But by focusing on these three key concepts - culture, technical fluidity, and management style - you can create a collaborative, innovative, and successful team.

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