Machine Learning & AI Engineer proficient in TensorFlow and PyTorch, with hands-on experience in CNNs, NLP, and LLM workflows. Experienced in fine-tuning large language models and developing scalable RAG-based systems for real-world applications.
"Building intuition for real-world data — from geospatial systems to neural architectures — one pipeline at a time."
Contributed 41,000+ verified geospatial edits over 12 months, focusing on road networks, intersections, and rail infrastructure across North Indian regions. Performed large-scale manual annotation using high-resolution satellite imagery.
Built an end-to-end AutoML pipeline using Python that automates the entire machine learning workflow. Engineered model selection logic that optimized hyperparameters for Random Forest and XGBoost, achieving a consistent 15% improvement over baseline.
Pursuing B.Tech with focus on Machine Learning, Computer Vision, and AI systems. Applying deep learning concepts to real-world problems through research projects and open-source contributions.
End-to-end AutoML pipeline automating data cleaning, visualization, and model selection. 15% improvement over baseline with optimized hyperparameters.
41,000+ verified geospatial edits across North Indian regions. Road networks, rail infrastructure, and satellite imagery annotation.
Open to internships, research collaborations, and projects in Machine Learning, Computer Vision, and AI systems.
