Aaryan Kamdar

Machine Learning Engineer Intern

About

Currently pursuing a Master’s degree in Computer Engineering at Virginia Tech, with an expected graduation in May 2026. Recently completed an internship as a Machine Learning Engineer at KM Facility Services, where they developed a real-time facial recognition attendance system using advanced CNN models, OpenCV, and SQL pipelines. Core competencies include deep learning, data models, and performance optimization for scalable systems. At KM Facility Services, contributed to reducing manual processes and improving payroll efficiency by implementing a robust end-to-end AI solution. Previous experience with the Martian Rover Team involved optimizing YOLO models for object detection in simulated Mars environments. Motivated to apply machine learning expertise to innovative projects, they thrive in collaborative settings and are committed to advancing technology through impactful work.

Country

United States

City

Santa Clara

Industry

Computer Software

Skill

Data Models, Matplotlib, Deep Learning, Multiple Projects Simultaneously, Large Language Models (LLM), Particle Image Velocimetry (PIV), Object Detection, Caffe, Programming Languages, Machine Learning Algorithms, Data Quality, WAMP, Neural Language Models, Data Pipelines, Optimization Models, Predictive Analytics, Modeling Languages, OpenCV, Transformer Models, PyTorch

Experience

Machine Learning Engineer Intern

KM Facility Services

2022-10 - 2023-5 · 8 mos

Mumbai, Maharashtra, India

• Built and deployed a real-time facial recognition attendance system using VGG-Face, OpenCV, and SQL pipelines, reducing manual attendance processing by 80% and improving payroll efficiency by 30% for a 100+ employee workforce. • Engineered end-to-end face image ingestion, preprocessing, and feature extraction pipelines enabling low-latency real-time inference. • Fine-tuned CNN models using transfer learning achieving >95% recognition accuracy across varied lighting and environmental conditions. • Optimized inference performance and system scalability to support real-time usage across multiple devices.

Machine Learning Engineer — Martian Rover Team

DJS Antariksh, Martian Rover Team

2021-10 - 2022-9 · 1 yr

Mumbai, Maharashtra, India

• Fine-tuned YOLO models for real-time object detection in Mars Yard simulation as part of the European Rover Challenge robotics team. • Built custom datasets and annotation pipelines using LabelImg and advanced augmentation techniques to improve detection robustness. • Evaluated and optimized model performance using mAP, precision, and recall for mission-critical object classification.

Education

Virginia Tech

Computer Engineering

2024-8 - 2026-5 · 1 yr 10 mos

Relevant Coursework: Advanced Machine Learning, Computer Vision, Applications of Machine Learning, Trustworthy Machine Learning, Non-Linearity & Predictability in Real-World Systems, Reinforcement Learning, Web Application Development, Statistics in Research.

University of Mumbai

Electrical and Electronics Engineering

2020-8 - 2024-5 · 3 yrs 10 mos

Relevant Coursework: Data Structures and Algorithms, Artificial Intelligence and Machine Learning, Database Management Systems, Cloud Computing, Linear Algebra, Probability and Statistics, Digital Image Processing.