An AI engineer turns existing AI capabilities into working systems for specific user needs. The role typically combines models, plug-ins, agents, and other tools rather than building every model from scratch. section 12.1.2
The role can change as a product matures: early work emphasizes selecting and assembling models and tools; later work shifts toward scalable infrastructure and operational management. section 12.1.1
An AI engineer differs from nearby roles in emphasis:
- Integrates prebuilt AI components into user-facing products for defined use cases. section 12.1.2
- Works alongside machine learning engineers, who focus on building and optimizing models. section 12.1.2
- May take on infrastructure responsibilities when dedicated MLOps support is unavailable. section 12.1.1 section 12.1.2
A lean AI team
In a lean team, one data scientist may collect data and train models while an AI engineer combines available components into a product. As the system grows, the engineer may need support or a new teammate to manage scalable infrastructure. section 12.1.1 section 12.1.2
Table 12.1 Five core areas of competence in an AI team
| Area |
Roles |
Description |
|---|---|---|
| Product management |
Product manager |
Identifies the opportunities, customer needs, and larger business objectives that the AI system will fulfill. The project manager also communicates the requirements and success criteria and guides a team in implementing this vision. |
| Software engineering |
Backend engineer Frontend engineer DevOps engineer Software architect |
Focuses on building, maintaining, and scaling the technical infrastructure of AI systems. This includes backend, frontend, and DevOps engineers, who handle the software foundation, and roles such as software architects, who design the overarching system structure. |
| Data science and AI development |
AI engineer Machine learning engineer Data scientist Data engineer MLOps engineer Prompt engineer |
Specializes in designing, training, and operationalizing AI models. This area includes machine learning engineers, data scientists, and MLOps engineers, as well as roles such as prompt engineers and AI engineers who integrate AI components into usable systems. |
| User experience |
UX researcher UX designer UI designer Conversational designer Content designer |
Ensures the AI system is user friendly, engaging, and aligned with user needs. UX and UI designers craft the functional and graphical experience, while conversational and content designers focus on interaction flows and clear communication. |
| Domain expertise |
Domain expert Data annotator |
Provides encoding-relevant expertise for the AI system. This includes the compilation and annotation of specialized datasets. |
Use the team-competence map to distinguish AI engineering from product management, software engineering, data work, and related AI roles. section 12.1.1
