Generative AI is poised to redefine industries, with McKinsey estimating it could automate up to 30% of hours worked in 2030. Yet, many organizations fall short of delivering on its promise. Pilots remain stuck, prototypes fail to scale, and ambitions are replaced by stalled initiatives.
Why the struggle? Often, it’s not a technology problem; it’s a talent problem. Success with GenAI requires more than technical skills. It demands strategically crafted teams, clarity on roles, and a phased approach to scaling. Organizations that invest in the right talent framework will unlock AI’s transformative potential—those that don’t risk being left behind.
The AI Engineer as a new breed of expertise
Developing GenAI applications, leveraging Large Language Models (LLMs), demands a different skill set than traditional machine learning or software engineering. Enter the AI Engineer—a hybrid role that combines software development expertise with a deep understanding of AI models.
Unlike general software engineers, who focus on building and maintaining systems, AI Engineers specialize in tasks like fine-tuning LLMs, designing retrieval-augmented generation (RAG) pipelines, and monitoring outputs for issues like hallucinations. The table below highlights key differences between these roles, providing clarity for organizations seeking to hire the right talent.
The diagram above is only indicative, as a crossover exists between both roles, with some AI Engineers being well-versed in traditional software development practices and vice versa.
While the AI Engineer brings specialized expertise in handling large language models and fine-tuning systems, generative AI is rarely a job performed by one person. Although AI Engineers may spearhead initial efforts by developing prototypes and fine-tuning models, the true value of AI often emerges only when solutions scale. This scaling requires a multidisciplinary team, with the AI Engineer acting as the anchor point for collaboration, enabling novel ways to interact with technology using natural language.
As projects grow in complexity, additional roles such as product managers, UI/UX designers, and developers become essential to optimize user experiences, manage intricate data workflows, and refine product-market fit. The phased team-building approach ensures that resources are allocated thoughtfully, aligning capabilities with the evolving demands of your GenAI initiatives.
AI team structure: Phased team composition
Effective GenAI team-building focuses on creating the right team for the right stage. The image below outlines team compositions across different phases of growth:
Phase 1: Start Small
- Team Composition: AI Engineer + Product Manager or Designer
- When to Use: Ideal for organizations piloting GenAI solutions or testing product-market fit. This lean setup allows you to quickly develop prototypes while keeping costs low. Often, the goal here is to demonstrate the added value of AI, potentially using a quick Streamlit application with a simple integration with an internal system to tailor it better to the organization. Accordingly, the AI Engineer should possess the skills to manage both AI-specific tasks and basic backend or frontend development needs.
Phase 2: Multidisciplinary Team
- Team Composition: Add UI/UX Designer, Frontend Developer, and Backend Developer / Data Engineer
- When to Use: Once GenAI is validated as a core business function or customer-facing product, expand the team to include specialists for scaling and enhancing the user experience.
- Additional nuance: In Phase 2, the Backend Developer can often handle basic data tasks, such as integrating APIs or managing straightforward database queries. However, as the data complexity grows, such as in projects requiring real-time processing, large-scale ETL pipelines, or high data governance standards, a dedicated Data Engineer is not something you should sleep on.
Phase 3: Distributed Teams
- Team Composition: Small, agile teams assigned to different products or components
- When to Use: For larger organizations where AI spans multiple products or workflows. Distributed teams maintain agility while ensuring expertise is applied where it’s needed most.
- Additional nuance: Distributed teams are effective for scaling AI initiatives across different products or workflows, but they can lead to siloed operations. To prevent misalignment, establish shared goals, communication channels, and cross-team collaboration frameworks.
Maintaining a pragmatic approach
The phasing clearly demonstrates that a specialized role like an AI Engineer is usually part of a multidisciplinary team. This is logical because AI is rarely offered as a standalone feature; it generally functions as a high-utility feature within a software product (e.g., translate this piece of text), or forms the backbone of how the application operates (e.g., AI being used in an IDE like Cursor to generate code automatically, which makes up the core product logic). Still, what makes a product successful, is a mix of technical prowess, human interaction, and great sales and marketing.
While the phased approach provides a helpful framework, AI team structure should evolve flexibly based on the project’s specific needs. For example, some projects might require a Data Engineer earlier than anticipated, or a Frontend Developer may be crucial in Phase 1 for rapid UI prototyping. A general pattern that we see in our projects is that early phases benefit from hiring versatile generalists (e.g., AI Engineers with capabilities across the stack), but as the project matures, incorporating specialists ensures depth in areas like UI/UX, backend scalability, and data management.
In addition, larger organizations will benefit from introducing operational roles, such as AI Ops teams, to monitor, maintain, and refine deployed systems. These teams ensure consistent performance and reliability across the distributed structure. With smaller projects, the backend developer can typically manage most of the project's operations if they dedicate weekly time to tasks related to server maintenance.
At Datalumina we are typically part of smaller innovation teams. As such, we often implement a more pragmatic approach that is based on individuals with diverse skill sets. It is not uncommon for our backend developer to also engage in data engineering tasks. The same holds true for an AI engineer also undertaking backend development. Generally, these areas are interconnected, as AI engineers must understand what is involved in deploying an AI application into production, which also necessitates some understanding of the infrastructure.
Considerations for business leaders
Building a GenAI team is about aligning talent with strategy, starting small, and scaling effectively. By understanding the unique skill sets required and thoughtfully composing teams, you'll increase your odds of building desirable AI products. So, depending on where you are and your needs for AI engineering, here are some questions you can begin asking yourself:
- What are my strategic priorities and projected business outcomes for AI?
- Is AI positioned as a differentiator, a core capability, or an operational efficiency tool within our organization?
- Where do we stand on the AI adoption curve—prototyping, scaling, or integrating AI across multiple products or workflows?
- Should we prioritize hiring versatile generalists to jumpstart initiatives, or do we need specialized roles immediately to ensure depth in critical areas?
- How can we allocate budget effectively to balance the costs of experimentation, scaling, and operational stability?
- Would outsourcing certain roles accelerate timelines or reduce risk, or is building in-house talent more advantageous for long-term growth?
Get Started with Your GenAI Team
Building a successful GenAI team doesn’t have to be overwhelming. By focusing on the right strategy, talent, and timing, you can unlock the full potential of AI for your business.
At Datalumina, we specialize in scaling GenAI development teams. Check out our case studies to see how we’ve helped other organizations succeed, and let’s explore how we can support your AI goals—contact us today.
