S
Shyam R
About
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Karnataka, India
Résumé
Jobs
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Software EngineerTCSDec 2025 - Current (6 months)Developed a Hospital Management System using Java, Servlets, and MVC architecture as part of an intensive 1-month hands-on development program, strengthening backend development and application design skills. Implemented end-to-end automation solutions using Python and Selenium, reducing manual workload by 40-60% and improving process consistency across recurring operational tasks. Designed and optimized machine learning-driven document analysis pipelines for logo, signature, and text detection using image processing and OCR, increasing document classification accuracy by ~85% and enabling automated generation of 100% standardized Excel-based reports. Collaborated within an Agile/Scrum team, using Git-based version control for efficient cod- Software Development Engineer InternNokia NetworksAug 2023 - May 2024 (10 months)Developed a Python-based tool to monitor Nokia Lightspan devices and automatically generate test cases from user-initiated configurations, reducing manual test creation effort by 60-70% and enabling fully repeatable configuration workflows. Built an intuitive web-based frontend using HTML, CSS, and Bootstrap, improving user accessibility and reducing operational steps by 40% through a streamlined interface. Integrated the frontend with backend automation workflows using the Flask framework, enabling reliable API communication and achieving ~95% workflow execution accuracy across configuration and test-generation processes.
Education
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- Integrated MTech - Computer Science and EngineeringVellore Institute of TechnologyJan 2019 - Jan 2024 (5 years 1 month)Vellore 8.57
Projects (professional or personal)
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- TTerrain ClassificationJan 2023 - Apr 2023 (4 months)Developed a terrain identification system using inertial sensor data (accelerometer and gyroscope) from prosthetic lower limbs, enabling accurate terrain detection without visual input. Preprocessed and analyzed multi-dimensional time-series data and implemented an LSTM-based deep learning model, achieving ~92% classification accuracy across multiple terrain types. Successfully classified terrains such as flat surfaces, inclines, declines, and uneven paths, enabling real-time assistive applications and improving adaptability and mobility of prosthetic devices for end-users.
- MMedical Ministrations: ChatbotAug 2022 - Nov 2022 (4 months)Developed a medical chatbot that predicts possible diseases based on user-entered symptoms, improving early self-assessment accuracy by ~85%. Implemented a dynamic symptom questioning system to identify co-occurring symptoms, enhancing prediction relevance and user experience. Integrated a web-scraping module to fetch up-to-date disease information from the National Health Portal, enabling the chatbot to provide recommendations from a dataset of over 1,000 diseases in real time.