Five AI portfolio projects that get interviews (and five that don't)

From reviewing hundreds of student portfolios: the projects that make a reviewer lean forward, the ones that get skipped, and the three things that turn any project into an interview.

Speaking at a MeetKats event
Speaking at a MeetKats event

I have reviewed hundreds of student portfolios through TechVerse Enclave, placement clinics and hiring at SCULT INDIA. Most contain the same three projects, and reviewers skip them in seconds. The ones that get interviews are not harder; they are more honest about a real problem.

Projects that get interviews

  1. A RAG system with published evaluation numbers over a corpus you understand — recall, faithfulness, refusal rate. The numbers are the differentiator.
  2. A fine-tuned small model with a base-versus-tuned comparison on a task you can explain in one sentence.
  3. A document or vision tool with a validation layer — extraction plus arithmetic checks, or recognition plus liveness. Shows you think about failure.
  4. Anything with ten real users and a changelog driven by their feedback. A shop, a club, an NGO.
  5. A tool you built for yourself and still use. Reviewers can tell.

Projects that get skipped

  1. The chatbot with no retrieval and no evaluation.
  2. The tutorial clone with the tutorial's dataset.
  3. The dashboard with fake data and no user.
  4. The "AI-powered" app where the AI is one prompt and the rest is a template.
  5. Anything without a README that says what problem it solves in the first line.

Three things that turn any project into an interview

  • A URL. Something running, even on a free tier, beats a perfect repository nobody can try.
  • Numbers. Users, evaluation scores, latency, cost. Any honest number.
  • A trade-off, stated. "We chose X over Y because Z" is the sentence reviewers remember.

How many projects

Three good ones beat twelve. One retrieval, one fine-tuning or vision, one with real users — the shape of the roadmap. Then spend the remaining time on the READMEs, because a reviewer gives each project about ninety seconds, and the README is where that time goes.

PortfolioProjectsCareerStudents
Pranjul Rathour
Pranjul Rathour
GenAI Engineer · Kanpur, Uttar Pradesh, India

GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).

Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.

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Pranjul Rathour, GenAI engineer in Kanpur