Rajat Gupta
ML Engineer
Building intelligent systems and solving real-world problems with machine learning.
Building intelligent systems and solving real-world problems with machine learning.
Check out some of my recent work.
Real-time anomaly detection system using Airflow, MLflow, and Streamlit.
LLM-powered IPL analytics assistant grounded in ball-by-ball match data.
End-to-end PD, LGD, Expected Loss, Segmentation & Anomaly Detection with FastAPI + Streamlit.
I’m a Data Scientist & ML Engineer with end-to-end experience building production-ready machine learning systems for risk modelling, anomaly detection, and high-frequency data pipelines. My background at CERN has trained me to work with large, complex datasets and deliver models that are fast, reliable, and explainable.
I enjoy working on the full lifecycle — from data engineering and feature design to model training, deployment, and monitoring. Recently, I’ve built an end-to-end anomaly detection pipeline on GitHub event streams (Airflow + MLflow + Docker + AWS), a real-time credit risk scoring system (FastAPI + XGBoost + SHAP), and a vector-search powered analytics assistant using Qdrant and Sentence-Transformers.
I’m comfortable collaborating with cross-functional teams, turning ambiguous problems into measurable outcomes, and deploying models that work in both batch and low-latency environments. Open to Data Scientist, ML Engineer, and ML Ops–oriented roles in Europe or remote.
ATLAS@LHC (CERN and University of Pittsburgh)
CMS@LHC (CERN and Panjab University)
Have a question or want to work together? Feel free to reach out!
Remote / Geneva, Switzerland