AI/ML Engineer & Data Scientist specializing in building production-ready machine learning systems. MS in Computer Science (Data Science focus) at University of Illinois, with expertise in end-to-end ML pipelines, MLOps, and scalable AI systems that solve real-world problems.
- ML Systems Engineering: Building scalable ML pipelines from research to production
- MLOps & Infrastructure: Deploying, monitoring, and maintaining ML models at scale
- Production ML: Creating robust, high-performance ML systems with proper logging and monitoring
- Real-time Inference: Low-latency ML services handling thousands of predictions per day
- Production ML pipelines with automated retraining and model monitoring
- Real-time inference systems with Docker deployment and scalability
- MLOps tooling and best practices for model lifecycle management
- Advanced MLOps platforms and model serving architectures
- Distributed ML training and edge deployment strategies
- ML system observability and performance optimization
- NYC Rental Price Prediction Pipeline - Complete MLOps pipeline with MLflow tracking, automated retraining, and API deployment
- Real-time Face Mask Detection - Production computer vision system achieving 98.2% accuracy with Docker deployment
- 🎓 MS Computer Science (Data Science) - University of Illinois (Expected 2026)
- 🎓 BS Computer Science - Penn State University (2024)
- 📜 AWS Machine Learning Fundamentals - Udacity Nanodegree
- 📜 AI Programming with Python - Udacity Nanodegree
- 📜 DeepLearning.AI Specialization - Machine Learning, Deep Learning, NLP
"The best ML model is the one running reliably in production." - MLOps Philosophy