5 AI Portfolio Projects That Get You Hired

Recruiters skim dozens of “ChatGPT clone” repos. Stand out with projects that show you understand data, evaluation, and deployment—not just API calls.

1. Production RAG over your own corpus

Ingest PDFs or markdown, chunk with metadata, hybrid search, cite sources in answers, include a 30-question eval JSON and scores in the README.

2. Tool-using agent with guardrails

Three tools max, step limit, structured logging, pytest on tool validation. Document one failure you fixed (infinite loop, bad SQL, etc.). Base architecture: first AI agent guide.

3. Fine-tuned small model for a narrow task

Not general chat—e.g., classify support tickets or extract fields from invoices. Compare base vs fine-tuned on a held-out set.

4. Real-time inference API

FastAPI + Docker + health checks + latency metrics. Optional: GPU autoscaling notes even if you deploy CPU-only for the demo.

5. End-to-end MLOps mini pipeline

Train a simple model, version data, register model artifact, deploy behind an API, monitor drift with a basic dashboard.

Presentation tips

• Architecture diagram on the first README screen.
• “What I would do with two more weeks” section—shows senior thinking.
• Link a short demo video or GIF.

Pair projects with the 6-month ML engineer roadmap if you are still building fundamentals.