CS student at VIT Bhopal building applied AI systems that are reproducible, testable, and measurable.
What I optimize for:
✓ Evaluation-first ML (baselines, metrics, ablations)
✓ Reliability-minded engineering (idempotency, recovery, clean interfaces)
✓ Practical deployment (Docker, CI, sensible defaults)
Roles I'm targeting:
AI/ML Engineer • Data Scientist • MLOps-minded roles
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Resilient public procurement crawler Extracts 50,000+ tenders from India's eProcure portal using Playwright with session persistence, heartbeat monitoring, and idempotent upserts. Tech: Python • Playwright • Pydantic |
Hybrid retrieval + rule layer + evaluation Answers legal questions using EU regulations via BM25 + FAISS hybrid retrieval, reranking, and refusal guards. Tech: Python • FAISS • BM25 |
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Real-time CV surveillance platform Multi-camera security system with React dashboard, Node.js backend, and Python AI worker (YOLOv8 + ByteTrack). Tech: React • Node.js • OpenCV • Docker Compose |
FastAPI model serving + SHAP explainability Fraud detection with XGBoost + SMOTE, serving via FastAPI with per-prediction SHAP attributions and Prometheus-style monitoring. Tech: Python • XGBoost • SHAP • FastAPI • Docker |

