Hapur, Uttar Pradesh, India
Engineer with experience building scalable technology solutions in high-impact telecom and media environments at Jio. Worked on JioBharat, contributing to feature rollouts like Search and Pagination on performance-constrained devices using C/C++, focusing on user experience at scale. In JRMS (Jio Rights Management System), developed microservices using Golang and GoKit, working with PostgreSQL, Docker, and DevOps teams to deliver reliable backend systems supporting business-critical workflows. Built an internal Node.js-based chatbot to streamline IMOC operational processes, improving internal efficiency. Contributed to JioTVPlus Search systems using Golang, RabbitMQ, and Typesense, enabling scalable and responsive search infrastructure. Interested in leveraging technology, product thinking, and business strategy to build scalable solutions with real-world impact.
Software Engineer – Jio Platforms Worked on scalable backend systems and high-impact telecom products serving large user bases: • Contributed to JioTVPlus Search backend using Golang, RabbitMQ, and Typesense to build scalable, low-latency search infrastructure. • Developed microservices for Jio Rights Management System (JRMS) using Golang (GoKit framework), integrating PostgreSQL and containerized deployments with Docker in collaboration with DevOps teams. • Built an internal Node.js chatbot to streamline IMOC operational workflows, improving response efficiency and reducing manual intervention. • Contributed to JioBharat feature rollouts (Search & Pagination) using C/C++, optimizing performance for low-cost devices. Focused on scalability, system reliability, and cross-functional collaboration across engineering and business teams.
• Built predictive models using Python (NumPy, Pandas, Scikit-learn) in Jupyter Notebook to analyze factors influencing restaurant success across different city locations. • Performed data cleaning, feature engineering, and exploratory data analysis to derive actionable insights. • Implemented and evaluated multiple machine learning models for prediction and performance comparison. • Developed end-to-end ML projects, strengthening understanding of model selection, validation, and real-world business applications.