Designed and developed a Predictive Maintenance Machine learning model for industrial oil pumps (PyTorch, scikit-learn, Python)

Worked on IoT pipeline to process real-time sensor data on AWS cloud. (MQTT, Mosquitto, C++)

Designed and Implemented One-Shot Learning Pipeline using Siamese Neural Network. (Keras, OpenCV, Python)

Designed and Developed architecture for OTA update (Over-the-air) pipeline using AWS lambda services. (AWS, C++, Bash, Docker)

Implemented web portal for Industrial customers to manage and control IoT devices across production lines. (Javascript, Java, Angular, AWS)

Implemented publisher-subscriber model in ROS to extract ground truth data from UUV simulator. (ROS, Python, C++)

Implemented a Linear Regression model on ground truth SONAR Image data to predict the submarine's range and heading.

Tested the reliability of the existing Linear regression model on SONAR Image Segmentation (SegNet) architecture by introducing noises in SONAR Images and comparing via ground truth values processed in separate pipelines. (PyTorch, Docker, Python, C++)

Modified and re-designed the Engine block manufacturing line to ramp up production capacity from 250 units/day to 500 units/day.

Designed and Developed machining fixtures to incorporate engine blocks machining across multiple models.

Worked on Marposs Automated Engine measurement Systems to improve Quality Control checks for Engine head and blocks before entering assembly lines.