Data Science + MLOps Engineer
Python | ML Pipelines | Drift Detection | Model Deployment
Building production-grade ML systems with automated monitoring and self-healing pipelines
About
B.Tech Data Science student passionate about building end-to-end ML systems that solve real-world problems. Specialized in MLOps, drift detection, and automated model retraining pipelines.
I bridge the gap between research and production—writing ML code that's deployment-ready with monitoring, testing, and self-healing capabilities built in.
Skills
Languages
- Python (Primary)
- Java
- C
ML / Data
- TensorFlow, Scikit-Learn
- NLP & Transformers
- Pandas, NumPy, EDA
- SQL & Data Pipelines
MLOps / Deployment
- Docker
- Apache Airflow
- Evidently AI, Model Monitoring
- Streamlit, FastAPI
Tools & Dev
- Git & GitHub
- Linux CLI
- DataBricks, PySpark
- CI/CD Pipelines
Featured Projects
Automated Self-Healing MLOps Pipeline
Problem
ML models degrade in production due to data drift. Manual retraining is reactive, causing business losses.
Solution
Built a production-grade monitoring system using Evidently AI for statistical drift detection (p < 0.05) with Apache Airflow orchestration. Auto-triggers retraining when drift detected.
Result
Reduced recovery time from days to minutes | 100% automated retraining | Production-ready monitoring dashboard
Other Notable Projects
Coding Practice & DSA
LeetCode Problems
Solved 100+ problems across arrays, trees, graphs, dynamic programming, and system design.
View SolutionsEducation
BTech in Data Science
SMIT (Sikkim Manipal Institute of Technology)
2024 - Ongoing
12th Grade - Science
DAV Public School
73%
2024
10th Grade
DAV Public School
77.6%
2022
Certifications & Achievements
Machine Learning Specialization
Coursera
Comprehensive ML algorithms and deep learning