•Engineered a highly scalable deep learning model for RBC in collaboration with a team of 4, utilizing TensorFlow and PyTorch to analyze user behavior and offer personalized suggestions.• Integrated the ML model in to the iOS application using Swift ensuring clean, organized, and code that is easy to understand and test.• Integrated features in the RBC iOS application such as pattern recognition to improve the user experience by 12% using deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs).• Utilized Git for version control and code collaboration, facilitating efficient teamwork on the project.• Communicated project progress and concerns with clients, showcasing strong interpersonal and communication skills

• Designed a fall detection android application in Kotlin using agile framework for SOTI. Designed Machine Learning algorithm using sci-kit learn library, TensorFlow and Pandas and integrated into the Android application while working in a team of 4.• Deployed k-means machine learning algorithm to improve the performance of the model by over 25%.• Collected over 2 Million data points from the sensors such as accelerometer and gyroscope inside the mobile device to detect a drop and assess the damage• Utilized strong knowledge of SQL to perform complex data analysis, extracting valuable insights and trends from large datasets. Leveraged Python and Spark to efficiently transform and process data, optimizing data workflows and enhancing data quality.• Documented the integration process and provided technical documentation to the development team, stream-lining future maintenance and updates.• Partnered with Product Management to understand user requirements and requirements, aligning development efforts with business needs.