Overview
Building AI features typically means integrating Large Language Models (LLMs) and other AI services into existing products. This is primarily engineering work: designing prompts, building user experiences, handling errors, managing API costs, and evaluating output quality.
This differs fundamentally from building AI/ML from scratch. Companies like Notion, Canva, and HubSpot added AI features with engineering teams using OpenAI and Claude APIs—they didn't hire ML researchers or train custom models.
For hiring, AI feature development requires strong software engineering skills. ML expertise is rarely necessary for AI feature integration. Focus on engineers who understand AI limitations, can design effective prompts, and know how to build products that use AI effectively.
What AI Feature Development Actually Looks Like
Most AI feature work is engineering, not machine learning. Understanding this distinction helps you hire the right people.
Real-World Examples
Notion added AI writing assistance using OpenAI APIs. Their engineering team built the feature—prompts, UX, error handling—without ML researchers.
Canva integrated AI image generation using third-party models. Engineering work included prompt design, moderation, and user experience.
HubSpot added AI-powered content suggestions through API integration. The challenge was UX and prompt engineering, not model training.
Key insight: These companies shipped AI features with software engineers, not ML specialists.
Build vs Integrate: The Right Approach
| Approach | When to Use | Skills Needed |
|---|---|---|
| API Integration | Most AI features | Software engineering, prompt design |
| Fine-tuning | Specific domain needs | Some ML + engineering |
| Custom Models | Rare, competitive advantage | ML research team |
Most Companies Should Integrate
API integration covers:
- Chat and conversational AI
- Text generation and summarization
- Content classification
- Semantic search
- Image generation
- Translation
Fine-tuning is needed when:
- Off-the-shelf models don't handle your domain
- You need specialized behavior
- Latency or cost requires smaller models
Custom models are for:
- Core competitive advantage in AI
- Problems existing models can't solve
- Companies with ML research capability
Skills for AI Feature Development
Essential Engineering Skills
Software Engineering Fundamentals:
AI features are software. Engineers need strong fundamentals—API design, error handling, testing, and system architecture.
API Integration:
Most AI features consume external APIs (OpenAI, Anthropic, etc.). Experience with API integration, rate limiting, and failure handling is essential.
Prompt Engineering:
Designing prompts that produce reliable outputs. Understanding how to structure instructions, provide context, and handle edge cases.
AI-Powered UX:
Designing user experiences around non-deterministic AI. Handling uncertainty, setting expectations, providing fallbacks.
Nice to Have Skills
ML Understanding:
Not deep expertise, but understanding of how LLMs work, their limitations, and what's possible. Helps with prompt design and expectation setting.
RAG Patterns:
Retrieval-Augmented Generation for grounding AI responses in your data. Increasingly common pattern for knowledge-based features.
AI Evaluation:
Methods for measuring AI output quality. Systematic testing and evaluation are harder than traditional software but essential.
Rarely Needed
Model Training:
Only for fine-tuning or custom models. Most AI features don't require this.
ML Research:
Academic ML knowledge is rarely needed for feature development.
Deep Learning Expertise:
Understanding neural network architecture isn't required to use AI APIs effectively.
Interview Approach for AI Feature Engineers
Questions That Reveal AI Feature Skills
"How would you handle unreliable LLM outputs in a user-facing feature?"
Good answers discuss:
- Error detection and fallback strategies
- User experience during failures
- Setting appropriate expectations
- Cost vs. quality trade-offs
"Design an AI feature for [use case]. Walk me through your approach."
Good answers include:
- Starting with user needs
- Considering AI limitations
- Prompt design thinking
- Evaluation strategy
- UX for non-deterministic output
"How do you evaluate whether an AI feature is working well?"
Good answers mention:
- Defining success metrics
- Test sets and evaluation
- User feedback loops
- Monitoring in production
Common AI Feature Hiring Mistakes
Mistake 1: Requiring ML Expertise
Why it's wrong: Most AI features are engineering work. ML expertise adds cost without adding value for API integration.
Better approach: Hire strong software engineers with interest in AI. ML background is a nice-to-have, not a requirement.
Mistake 2: Expecting Deterministic AI
Why it's wrong: LLMs are non-deterministic. Engineers who don't understand this build brittle features.
Better approach: Evaluate understanding of AI limitations. Look for experience handling uncertainty.
Mistake 3: Ignoring Cost Optimization
Why it's wrong: AI API costs can explode. Features built without cost awareness become unsustainable.
Better approach: Ask about cost management in AI systems. Look for awareness of caching, model selection, and usage optimization.
AI Feature Engineering in Practice
The Engineering Challenges
Building AI features involves solving real engineering problems:
Prompt Engineering:
Designing prompts that produce consistent, useful outputs. This requires:
- Understanding how to structure instructions
- Providing appropriate context and examples
- Handling edge cases and unexpected inputs
- Iterating based on real-world results
Error Handling:
AI outputs can fail in ways traditional software doesn't:
- Model timeouts and rate limits
- Inappropriate or harmful outputs
- Hallucinations and factual errors
- Format violations in structured outputs
Cost Management:
AI API costs can grow quickly:
- Caching strategies for repeated queries
- Model selection based on task complexity
- Token optimization in prompts
- Usage monitoring and alerting
Building AI-Powered User Experiences
Setting User Expectations:
Users need to understand AI limitations:
- Clear indication when AI is generating content
- Appropriate confidence indicators
- Easy correction and feedback mechanisms
- Graceful degradation when AI fails
Latency Considerations:
AI responses take time:
- Streaming responses for better perceived performance
- Loading states that set appropriate expectations
- Timeout handling and retry strategies
- Caching for common queries
Team Composition for AI Features
Who You Actually Need
Software Engineers (Primary):
Most AI feature work is engineering. Strong software engineers who understand AI limitations are more valuable than ML specialists who can't ship products.
Product/UX Designers:
AI features need thoughtful UX design. How do you present uncertain outputs? How do users correct mistakes? These are design problems, not ML problems.
ML Engineers (Sometimes):
Only needed for fine-tuning, custom models, or complex ML pipelines. Most AI features don't require this.
Interview Focus Areas
Practical AI Experience:
- Have they built AI-powered features before?
- Do they understand the difference between demos and production?
- Can they discuss trade-offs in AI system design?
Engineering Fundamentals:
- API integration and error handling
- Performance optimization
- Testing strategies for non-deterministic systems
Product Thinking:
- How do they approach AI UX challenges?
- Do they understand user expectations?
- Can they balance AI capabilities with user needs?