Aaqib Bashir
Data Scientist, AI Instructor & Deep Tech Engineer
Aaqib Bashir is a Data Scientist and AI Instructor specializing in applied machine learning, deep-tech development, and scalable AI training. At Chinar Quantum AI, he designs end-to-end learning programs, builds real-time AI applications, and mentors learners in deploying production-grade models across intelligent systems.
His work spans data analysis, clustering, predictive modeling, NLP pipelines, and edge-AI development using PyTorch, TensorFlow, and modern deployment stacks. He integrates strong physics foundations with engineering depth to build impactful and industry-ready AI solutions.
Experience
Deep Tech Engineer & AI InstructorPresent
Chinar Quantum AI
- Designed and delivered comprehensive AI curriculum from fundamentals to advanced real-time systems.
- Mentored learners in applying AI to robotics, IoT, cloud-based systems, and edge computing.
- Built and deployed AI applications using TensorFlow, PyTorch, and optimized model pipelines.
- Enabled learners to design production-ready solutions with real-time data processing.
- Collaborated with industry experts to create future-focused training material.
Data ScientistPresent
Chinar Quantum AI
- Performed advanced clustering using DBSCAN, HDBSCAN, PCA, and SVD for pattern discovery.
- Built predictive models including Random Forest, Gradient Boosting, and Logistic Regression.
- Automated preprocessing, feature engineering, and tuning using Scikit-learn and NumPy.
- Developed and deployed ML applications using Flask, FastAPI, Docker, and Streamlit.
- Collaborated with cross-functional teams to create end-to-end ML workflows.
Junior Data Scientist Intern
Chinar Quantum AI
- Built dashboards and EDA workflows to uncover insights in complex datasets.
- Supported development of internal ML tools and automation scripts.
Data Scientist Intern
Himal AI Private Limited
- Worked on practical ML model development for real-world datasets.
- Implemented data cleaning, modeling, and evaluation pipelines.
Education
Master's in Physics
University of Kashmir
Projects
Document Classification using Spectral Clustering & DBSCAN
Developed unsupervised clustering workflows for document grouping using spectral clustering, DBSCAN, PCA, and feature engineering.
Predictive Maintenance System
Implemented predictive models using sensor logs and anomaly detection for maintenance forecasting.
RFP Data Augmentation System
Built an NLP-based data augmentation pipeline for RFP document expansion and variability generation.
Thin Films: Preparation, Properties & Applications
Physics research project analyzing synthesis, properties, and use-cases of thin-film materials.