Why Standard Demos Fail
Any candidate can put together a Streamlit app that calls an OpenAI endpoint within an hour. AI employers and lead engineers see right through this. They aren't looking for people who can simply send prompts, but engineers who can build systems that scale, handle failures, limit latency, and manage data privacy.
Want to deepen your theoretical knowledge before building your portfolio? Check out our guide at llmnet.nl/leren/ for fundamental engineering learning paths.
1. A Local RAG System with Hybrid Search and Evaluation
What It Demonstrates
You understand that vector searches are not a silver bullet and that traditional keyword searches (BM25) are often necessary for precision. You demonstrate that you can set up RAG pipelines without blindly relying on external managed services.
Key Components
- Implementation of hybrid search (Dense vectors + Sparse BM25 via rank fusion).
- Local orchestration with open-source models (e.g., via Ollama or llama.cpp).
- An automated evaluation step (e.g., Ragas or deepeval) to measure hallucinations and context relevance.
How to Document This
Write a technical README on GitHub with an architecture diagram. Share in a short blog post the challenges you encountered with chunking strategies and how you measured retrieval accuracy using real test datasets.
2. Asynchronous AI Agent Pipeline with Robust Error Recovery
What It Demonstrates
AI models are non-deterministic and APIs fail regularly. This project proves that you can build production systems that can take a beating, manage state, and autonomously execute complex workflows.
Key Components
- Event-driven architecture or task queue (e.g., Celery, Temporal, or Redis Streams).
- Strict output validation (with Pydantic) to guarantee that agent steps never return malformed JSON.
- Automatic retries with exponential backoff and logging of agent reasoning steps (tracing).
How to Document This
Post a short demonstration video on your repo showing how the system recovers when an LLM call fails or returns incorrect data. Document the state machine in a clear state diagram.
3. Cost- and Latency-Optimized LLM Gateway / Proxy
What It Demonstrates
When scaling, costs and response times can quickly get out of hand. As an AI engineer, you need to be smart about resource allocation. This project shows that you think from both a business and operational perspective.
Key Components
- A caching layer (semantic caching) based on embeddings to handle identical or similar queries locally.
- Dynamic model routing: sending simple queries to a small, fast local model; complex tasks to a heavier model.
- Rate-limiting and token budgeting per user or API key.
How to Document This
Show benchmarks in your repository. Compare the latency and costs per thousand requests with and without your caching and routing mechanism. Hard numbers from your own benchmarks will convince any hiring manager.
How to Present Your Projects
An employer does not judge you on the amount of code, but on the quality of your documentation and your technical choices:
- Make the code public: Clean, tested code with type hints and a clear directory structure.
- Describe trade-offs: Don't just explain *what* you built, but especially *why* you chose certain technologies and which limitations you consciously accepted.
- Avoid AI spam: Make sure you understand every line of code. During a technical interview, you will be asked about the architectural decisions.