AI Engineer (Building) · 3rd Year CCE @ Amrita Vishwa Vidyapeetham · 2028
Building GenAI applications — RAG pipelines, multi-provider LLM backends, full-stack AI tools
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Open to AI Engineer internships & collabs
| Project | What it does | Stack | Status |
|---|---|---|---|
| CLI Multi-Provider AI Chatbot | Provider-agnostic LLM backend - Groq, Gemini, OpenRouter through one adapter interface. FastAPI REST API, 10 pytest tests, streaming, JSON mode | Python · FastAPI · Groq · Gemini · OpenRouter · pytest | ✅ Live |
| AI Study Assistant | RAG pipeline from scratch - no LangChain, no vector DB. PDF extraction → chunking → keyword retrieval → LLM generation | Python · Gemini API · PyPDF2 | ✅ Live |
| SpeechCraft AI | Real-time speech coaching - live recognition + AI feedback + gamification for dyslexia users | JavaScript · Web Speech API · Groq API | ✅ Live |
GenAI / LLMs RAG Pipelines · LLM APIs · Prompt Engineering · Adapter Pattern
Groq API · Gemini API · OpenRouter · LangChain (learning)
Backend FastAPI · REST APIs · Pydantic · async/await · python-dotenv
Languages Python · Java · JavaScript · SQL
Frontend React · Tailwind CSS
Testing & CI pytest · unittest.mock · GitHub Actions
Tools Git · Docker (basics) · Postman · IntelliJ IDEA · VS Code
- Multi-provider architecture — not hardcoded to one LLM API. Groq, Gemini, and OpenRouter behind one interface. Swap providers without touching app logic.
- RAG without frameworks — built retrieval from scratch to understand what LangChain and LlamaIndex actually hide. Chunking, scoring, Top-K, all manual.
- Tests that verify claims — 10 pytest tests with mocked providers. Defensive JSON parsing isn't just mentioned in the README — it's tested.
- CLI → REST API without rewrite — same provider adapters power both. That's what separation of concerns looks like when it actually works.
- Phase 2 — RAG with vector databases (Pinecone / ChromaDB)
- Phase 3 — AI Agents with LangGraph
- Streaming responses + persistent conversation history
- Deploying full-stack AI app with live URL