- ๐ป Computer Vision / ML Engineer focused on building end-to-end machine learning systems โ from data preparation and modeling to evaluation, APIs, and deployable demos
- ๐ฏ Open to junior and internship opportunities in Machine Learning, Computer Vision, and Applied AI
- Real-time computer vision systems
- Detection, tracking, and face recognition pipelines
- Applied machine learning for structured and visual data
- ML projects with clear evaluation, reproducibility, and practical use cases
- FastAPI-based face recognition system with real-time detection, tracking, unknown-face handling, event logging, and Dockerized setup
- Tech: Python, FastAPI, OpenCV, FAISS, Docker
- Repository
- End-to-end people detection workflow including annotation cleanup, preprocessing, and dataset preparation for model training
- Tech: Python, OpenCV, YOLO
- Repository
- End-to-end tabular ML project with EDA, preprocessing, model comparison, threshold tuning, and error analysis.
- Tech: Python, Pandas, Scikit-learn, SHAP
- Repository
- ๐ Languages: Python (primary), Java (software development background)
- ๐ ML/Data: Pandas, NumPy, Polars, Scikit-learn, SciPy
- ๐ค Deep Learning: PyTorch, TensorFlow
- ๐๏ธ Computer Vision: OpenCV, YOLO, MediaPipe
- ๐งฎ Databases: MySQL, PostgreSQL
- โ๏ธ Tools: Git, Docker, Linux