An AI product is still a software product.
It needs application architecture, APIs, databases, authentication, testing, deployment, monitoring, and a usable interface. The difference is that it also has AI-specific components that introduce new engineering and evaluation requirements.
So when building an AI development team, the question shouldn't simply be “How many AI engineers do we need?”
The better question is:
What capabilities does this product require?
Step 1: Define the AI Architecture
Before hiring, determine what kind of AI system you're building.
For example, is it:
- An application using an LLM API?
- A RAG application?
- A recommendation system?
- A predictive ML product?
- A computer vision application?
- A custom model?
- An AI feature inside an existing SaaS product?
This distinction changes the team requirements significantly.
A simple LLM-powered feature may require strong application engineers and AI integration skills.
A custom machine learning system may require specialized ML and data expertise.
Step 2: Identify the Core Engineering Skills
Most AI products need several technical capabilities.
AI/ML Engineering
Responsible for the AI layer, including model integration, evaluation, RAG, prompts, inference workflows, and model-related experimentation.
Backend Engineering
Responsible for APIs, business logic, databases, authentication, integrations, and application services.
Frontend Engineering
Needed when users interact directly with the AI product through a web or mobile interface.
Data Engineering
Important when the product depends on significant data pipelines, transformations, storage, or continuously updated datasets.
Infrastructure/Cloud
Becomes important for deployment, scaling, observability, security, and operational reliability.
A single engineer may cover several of these responsibilities in an early-stage product.
Step 3: Add Product Ownership
Engineering alone doesn't define a successful product.
Someone needs to answer:
- Which problem are we solving?
- Who is the user?
- Which features matter first?
- What should be measured?
- What should be postponed?
A product manager or founder may take this responsibility in an early startup.
The important part is that product ownership exists, even if there isn't a dedicated product manager.
Step 4: Treat AI Evaluation as a First-Class Requirement
Traditional software testing isn't enough for many AI applications.
An AI system can technically run while still producing poor results.
The team should define:
- What counts as a correct response?
- Which cases are high risk?
- How will output quality be evaluated?
- How often should the system be tested?
- When should a human review the result?
For an AI assistant, you might create a representative evaluation dataset and test changes against it before deployment.
Step 5: Decide What You Don't Need
Avoid hiring for technologies that the product doesn't require.
For example, using an existing foundation model through an API doesn't automatically mean you need a team of researchers developing models from scratch.
Similarly, a small MVP may not require a dedicated MLOps engineer, data scientist, security engineer, and DevOps engineer as separate full-time roles.
The early team can often share responsibilities.
Step 6: Choose the Initial Team Structure
A small AI product might start with:
- Product ownership
- AI/software engineering
- Product design
- Part-time infrastructure support
As usage and complexity increase, additional specialization can be added.
The important thing is to avoid confusing future organizational needs with current product requirements.
Step 7: Decide What to Build vs. Buy
AI products can rely on many existing services.
Instead of building everything internally, evaluate whether to use:
- Foundation model APIs
- Managed databases
- Cloud AI services
- Authentication platforms
- Vector databases
- Monitoring tools
- Existing data services
Build internally where it creates product value or differentiation.
Use existing infrastructure where rebuilding it would add cost without improving the product.
Step 8: Define Ownership Before Development Scales
As the team grows, ownership becomes critical.
Someone should be responsible for:
- AI quality
- Data quality
- Security
- Infrastructure
- AI costs
- Production monitoring
- User feedback
- Product requirements
Without clear ownership, problems can fall between engineering, product, and data teams.
Internal Team or External Support?
There isn't a requirement to build every capability internally.
An external AI engineering team can help with implementation when internal engineering capacity is limited. An AI consultant can also help with architecture, use-case selection, and technical planning.
BuildingBlocks Consulting is one example of a team that combines AI consulting with engineering capabilities.
Whether you use internal employees, external specialists, or a combination, the same principle applies:
Build the team around the product architecture and business requirements.
The Team Should Evolve With the Product
Your first AI product team doesn't need to look like the engineering organization you'll have two years from now.
Start with the capabilities required to validate and build the product.
Then add specialized expertise when the product introduces new requirements around scale, data, security, infrastructure, model development, or operational reliability.
The best starting point is not a fixed list of job titles. It is a clear understanding of what the product needs to work.
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