AI Engineering Portfolio
Definition
An AI Engineering portfolio showcases projects demonstrating practical skills in building AI applications, including RAG systems, AI agents, LLM integrations, and production-ready AI features.
What to Include
Essential Projects:
- RAG Application: A working retrieval system with chunking, embeddings, and vector search
- AI Agent: An agent that uses tools to accomplish tasks autonomously
- Production Feature: An AI-powered feature with proper error handling, evaluation, and monitoring
Bonus Projects:
- Multi-agent system with coordination
- Voice or multimodal application
- Open-source contribution to AI tools
- Fine-tuned model for a specific task
How to Present
For each project, document: the problem it solves, architecture decisions and why you made them, challenges encountered and how you solved them, evaluation metrics and results, and what you’d do differently. Code should be clean, well-documented, and include a README with setup instructions.
Quality Over Quantity
Three well-executed projects with thorough documentation beat ten half-finished repos. Interviewers want to see that you can ship complete solutions, not just follow tutorials. Focus on projects that demonstrate judgment and problem-solving, not just technical implementation.