AI Engineer Resume and Portfolio: 6 Projects That Get Interviews

Hundreds of applicants have a chatbot in their portfolio. What stands out is evidence: a real problem, measured results, honest limits and a deployed link. Use these six projects and the README template.

What makes an AI project stand out

• A specific user and problem, not "a chatbot"
• Real or realistic data, not three hand-picked examples
• An evaluation with numbers (LLM evals explained)
• Cost and latency shown
• Failure analysis: what goes wrong and why
• A deployed app and a short demo video

Six projects

1. Document Q&A with citations

Use RAG over a document set you know (policies, a syllabus, a manual). Show: retrieval recall@5, faithfulness, sample failures and citations in the UI.

2. Structured data extraction

Turn invoices, resumes or emails into validated JSON with a review step for low-confidence items. Show: field-level accuracy and how review cuts errors.

3. A tool-using assistant

Build a small agent with two or three safe tools, step limits and approval for risky actions. Show: success rate on 30 scenarios, including attacks.

4. Natural-language analytics

Let users ask questions about a sales or sports database. Convert to read-only SQL, check it and show the query. Show: an eval of 50 questions and safe refusals.

5. Search and re-ranking study

Compare keyword, vector and hybrid search, with and without re-ranking, on the same dataset. Show: a results table and your recommendation. This proves you can run experiments.

6. Cost and reliability project

Take one of the apps above and improve it: caching, a smaller model for easy cases, retries and fallbacks. Show: before and after cost, speed and quality.

README template

1. Problem and user
2. Demo link and a 2-minute video
3. Architecture diagram
4. Data used and how you handled quality and privacy
5. Evaluation: dataset size, metrics and a results table
6. What failed and what you changed
7. Cost and latency
8. How to run it in a few commands

Resume bullets

• Weak: "Built a RAG chatbot with LangChain and OpenAI."
• Strong: "Built a policy Q&A assistant with hybrid retrieval and citations; improved recall@5 from 0.72 to 0.88 and cut cost per query by 35% through caching and a smaller model for simple questions (100-question eval set)."

The numbers in the example are only to show the style. Do not copy them. Use your real, measured results, and if the data is self-made, say so.

Resume structure

Put Projects near the top for freshers. Include links to GitHub and demos. Use the fresher resume guide or the experienced engineer resume guide, then check with the ATS checklist.

GitHub tips

Keep repositories clean and documented. Pin your best three. See GitHub profile and portfolio guide.

Mistakes to avoid

• Tutorial clones with no changes
• No tests, no evals, no failure cases
• Leaking API keys in the repository
• Claiming results you did not measure
• Listing ten tools you cannot explain

Next steps

• AI engineer study plan
• AI engineer interview questions
• Considering customer-facing work? See FDE portfolio projects