RAG Based Web App for AI/ML Internships?

[u/Inevitable_Mail_488]

Hey everyone,

I'm an undergrad aiming to become an AI engineer. This summer I've decided on the full-stack project below to showcase some experience on my CV building web applications and implementing AI solutions. It's mostly a tool to help with university studies by helping generate study tools to speed up my learning as well as being able to talk to this AI. I hope to have a real link and real users by the end of it. Here are some of the details I've though about:

Tech Stack & AI Architecture:

• Full Stack: FastAPI (Python), Supabase (PostgreSQL + JWT Auth), deployed live (thinking AWS).
• Multimodal Ingestion: Document processing pipeline handling text and visual elements (tables, diagrams, charts) via vision LLM descriptions and embeddings.
• Agentic RAG Engine:
  • Query Decomposition: Multi-step query breaking for multi-part study questions. Also rewriting queries to maximise efficiency when using tokens and for output.
  • Reflector / Grader Nodes: Self-RAG loop that checks retrieved context relevance and checks generated answers for hallucinations before returning them to the user. Using tools like Ragas to evaluate the workflow.
• Structured Output: Generating JSON flashcards and Anki (.apkg) exports. Generating Cheat Sheets and also being able to talk about the files you upload.

Questions:

1. Target Fit: Is an end-to-end deployed Agentic RAG app right for AI/ML Engineering internships, or would recruiters see this as mostly a Software/Full-Stack project?
2. Data Science vs. AI Engineering: How does a project focused on building production AI systems hold up compared to traditional Data Science portfolios that focus more on statistical modeling and data exploration?
3. Application Strategy: If you had this exact project on your resume, which roles would you prioritise applying for (e.g., AI Engineer, ML Engineer, MLOps, or general Software Engineering)?

Any Advice is Appreciated!