🎓 BS Data Science & Applications — IIT Madras (Graduated May 2026) 🤖 Building production ML and AI systems — MLOps pipelines, REST APIs, GenAI applications 🔍 Open to Data Science / Data Engineer / ML Engineer / AI Engineer roles
- Trained 4 classifiers (Random Forest, SVC, AdaBoost, Decision Tree) on LAPD crime data — Random Forest was the best performer
- Engineered time-based, geographic, and demographic features; applied GridSearchCV hyperparameter tuning
- Conducted feature importance analysis to identify key predictors across location, timing, and incident type
- Built an end-to-end ML observability pipeline with SHAP explainability across demographic groups and data drift detection (KS, PSI tests) triggering automated retraining alerts
- Automated the full pipeline (train → evaluate → explain → monitor) via GitHub Actions CI, cutting manual intervention to zero
- Integrated MLflow for experiment tracking, model registry, and reproducibility
A production-deployed REST API for managing students, courses, enrollments, and grades with JWT-based authentication.
- Built full CRUD routes for Students, Courses, Enrollments, and Grades using FastAPI with auto-generated Swagger docs at
/docs - Implemented JWT authentication (python-jose + passlib/bcrypt) — all routes are protected and require a valid token
- Used SQLModel (Pydantic + SQLAlchemy) for ORM, with duplicate checks for emails, course codes, and enrollments; marks validated between 0–100
- Deployed live on Railway with a
Procfile+UvicornASGI server
- Analyzed sales and inventory data for a retail mall (~₹2,00,000/month revenue) to identify operational gaps
- Identified top product categories contributing ~70% of total sales via Pareto analysis; surfaced seasonal trends (November peak, January low)
- Delivered 6 actionable recommendations projected to reduce capital blockage by 20–25%
- Built a multi-role quiz platform (V1) with admin and user roles, quiz creation, scoring, and result analytics
- Extended to V2 with REST API, Vue.js frontend, Celery background jobs, and interactive performance dashboards
🎓 BS Data Science & Applications — IIT Madras (2021–2026, CGPA: 8.1)
Workshops — IIT Madras
| Workshop | Focus |
|---|---|
| Mathematical Foundations for ML | Linear algebra, probability, optimization |
| Machine Learning Techniques | Supervised & unsupervised learning |
| Deep Learning & NLP (Prof. Mitesh Khapra / AI4Bharat) | Neural networks, transformers, NLP |
| Understanding Google Cloud Platform | GCP, BigQuery, Vertex AI |
| NumPy and Pandas Workshop | Data manipulation from scratch |
Certifications
- 📜 Mathematics - Basics to Advanced for Data Science and GenAI — Udemy
- 📜 Python for Data Science & AI — Coursera
- 📜 Intro to Git and GitHub — Coursera
- 📜 SQL (Basic, Intermediate) — HackerRank
| rajeev90767@gmail.com | |
| linkedin.com/in/rajeev245 | |
| 🐙 GitHub | github.com/21f3001527 |
| 📍 Location | Patna, Bihar, India |
| 📞 Phone | +91 7903766709 |
"Ship real systems. Learn by building."