Ranjith Kumar Srinivasulu

A highly motivated and resourceful computer science student with a strong foundation in software engineering principles, proficient in multiple programming languages and technologies. Demonstrates excellent communication and collaboration skills, with a proven ability to document processes and perform thorough testing. Possesses a keen analytical mind and problem-solving capabilities, eager to contribute to innovative projects and enhance technical skills.

• PG Diploma in Computer Applications, Completed a Postgraduate Diploma in Computer Applications, gaining comprehensive knowledge in software development, database management, and computer networks. The program covered key areas such as programming languages (Java, C++), web development, system analysis, and design. Developed practical skills through hands-on projects, including application development, database design, and IT infrastructure management. This certification enhanced technical proficiency and provided a strong foundation in computer science principles, preparing for advanced roles in IT and software engineering.
• Intro to Generative AI Course, Gained foundational knowledge in generative models and their applications. Learned key concepts such as Generative Adversarial Networks (GANs), variational autoencoders, and transformer models. Gained hands-on experience with data preprocessing, model training, and evaluation using popular frameworks like TensorFlow and PyTorch. Developed skills in generating synthetic data, creating realistic images, and enhancing text generation. This certification equipped with essential skills to leverage generative AI techniques in various projects, enhancing creativity and innovation in problem-solving.

• Software Development
• Automation and Testing
• Web Development
• Data Analysis
• Cybersecurity
• Root Cause Analysis
• Process Improvement

Deep Learning for Industrial KPI Prediction, Developed a deep learning model to predict Key Performance Indicators (KPIs) in an industrial setting, enhancing operational efficiency. Utilized advanced neural networks to analyze historical data, identify patterns, and forecast future performance metrics. Implemented data preprocessing techniques, feature engineering, and model optimization to improve accuracy. Integrated the model into existing systems for real-time monitoring and decision support. Achieved significant improvements in predictive accuracy, leading to more informed decision-making and resource allocation. This project showcased skills in machine learning, data analysis, and application of AI in industrial contexts.