•Developed and fine-tuned Retriever Augmented Generation (RAG) models using the LangChain framework, integrating retrieval techniques with generative models to enhance content generation. This work involved optimizing retrieval strategies and model architecture to create highly relevant, context-aware outputs in a variety of use cases.•Implemented RAG Fusion and multi-query techniques to boost the accuracy and relevance of the generated content. These techniques allowed for a more comprehensive and nuanced retrieval process, leading to better alignment between queries and results.•Developed a custom program to measure the accuracy of RAG models, implementing various metrics to evaluate performance and optimize model effectiveness. This helped in fine-tuning model parameters and provided actionable insights for continuous improvement.•Scraped data using APIs to gather and preprocess large datasets, ensuring high-quality input data for model training and evaluation. Employed advanced data cleaning techniques and feature engineering to prepare the data for downstream machine learning tasks, enhancing the overall performance of the RAG models.

•Developed and deployed the front end of a MERN application, focusing on creating a highly responsive and visually engaging user interface. Ensured seamless user experiences across various devices and screen sizes, prioritizing accessibility and ease of use.•Collaborated with cross-functional teams (product managers, designers, and backend developers) using agile methodology. Actively participated in sprint planning, daily stand-ups, and retrospectives, ensuring clear communication and alignment with project goals.•Utilized ReduxJS for efficient state management, improving application performance by optimizing data flow and component re-rendering. Implemented best practices to maintain a scalable and maintainable codebase.•Conducted rigorous testing, including unit, integration, and end-to-end tests, to ensure the application’s stability and reliability across different environments. This led to a 100% successful production deployment without any critical issues.•Collaborated with clients to design target architecture, migration plans, and detailed operation manuals for the successful migration of over 150 Windows and Linux/Unix-based applications to AWS and Microsoft Azure. These efforts led to streamlined, efficient, and secure migration processes, ensuring minimal downtime and optimal system performance.•Initiated and directed comprehensive data collection, analysis, and presentation for migrating 10 data centers across multiple countries. This initiative resulted in a seamless transition with minimal data loss, enabling uninterrupted operations for clients during and after the migration process.•Utilized ETL processes and advanced Excel functions for data cleansing, transformation, and visualization, improving the accuracy and clarity of data analysis. This approach ensured that data was optimized for migration, and insights were effectively communicated to stakeholders for informed decision-making.

•Gained hands-on experience in programming ATmega328 microcontrollers using C. During this internship, I deepened my understanding of embedded systems and hardware-software integration.Built and developed projects, including a line-following robot that used sensors and motor control algorithms to autonomously navigate paths, and a smoke detector, which utilized sensor data for real-time detection and alert systems.

•Gained in-depth knowledge of mobile communication technologies such as 2G, 3G, 4G, GPRS, and broadband systems. This internship provided hands-on experience in understanding the underlying architecture and protocols that enable modern telecommunications.•Explored the technical aspects of network operations, signal processing, and data transmission, allowing me to develop a solid foundation in mobile communications and broadband infrastructure.