Building production-grade AI systems: retrieval, agents, evals — shipped with clean APIs and real metrics.
- 🎓 B.Tech CSE @ VIT Bhopal (2023–2027) • CGPA 8.45 • internship-ready
- 🤖 AI focus: multi-agent pipelines, RAG with hybrid retrieval + evals, PEFT fine-tuning
- 🧩 Full-stack: Next.js UIs + FastAPI backends + Docker-deployed architecture
- 📊 I report real numbers — ROUGE, BERTScore, accuracy, latency — not just "built a model"
- Multi-agent systems — LangGraph supervisor pattern, async job queues, LLM-driven routing, checkpointing
- RAG pipelines — hybrid search (BM25 + dense), RRF fusion, cross-encoder reranking, LLM-as-Judge evals
- Fine-tuning — PEFT/LoRA on FLAN-T5 and RoBERTa with reproducible metrics (ROUGE, BERTScore, F1)
- Production habits — Docker containers, FastAPI services, LangSmith tracing, MLflow/W&B experiment tracking
Languages
ML / DL
LLM / Agent / Retrieval
MLOps / Observability
Backend / Full-Stack / DevTools
- Gaming (PC / mobile)
- Music (EDM, lo‑fi, metal)
- Manhwa / webtoons
- Sci‑fi & thrillers
Built with curiosity, caffeine, and commits.