Headline

Intern at Defence Research and Development Laboratory (DRDL) - DRDO

About

I am a passionate and curious Computer Science student specializing in Artificial Intelligence and Machine Learning, with a strong interest in building real-world, impactful solutions. I enjoy working at the intersection of software development and AI, where I can design systems that are not just functional, but also intelligent and scalable. My recent experience as an intern at Centre for Artificial Intelligence & Robotics (CAIR), DRDO gave me the opportunity to work on integrating text processing workflows with MLOps, strengthening my understanding of end-to-end machine learning pipelines and deployment strategies. I have hands-on experience in Python, Java, C++, and web technologies (HTML, CSS, JavaScript, React), along with a solid foundation in data structures, algorithms, and database systems. I’ve worked on projects ranging from AI-powered applications and machine learning models to full-stack web development, always focusing on solving practical problems. What defines me is my consistent learning mindset, problem-solving approach, and ability to adapt quickly to new technologies. I actively challenge myself through coding platforms, projects, and continuous exploration in AI and software engineering. I am currently looking for opportunities where I can contribute, learn, and grow as a developer and AI enthusiast while working on innovative and meaningful projects.

Location

Bengaluru

AI stack

antigravity

claude code

claude

Work experience

Intern at Defence Research and Development Laboratory (DRDL) - DRDO, May 2025 - Jul 2025 · 3 mos, During this internship, I worked on the project titled “Experimentation on Integrating Text Processing Workflow on MLOps”, where I gained hands-on experience in building and optimizing text processing pipelines and understanding real-world applications of MLOps in AI systems.

Intern at DRDO CAIR Lab, May 2025 - Jul 2025 · 3 mos, Addressed scalability and reproducibility challenges in large-scale document clustering by designing an end-to-end MLOps pipeline with data validation, versioning, monitoring, and experiment tracking. Improved text representation for document clustering by applying TF-IDF and N-gram feature engineering, enabling more effective and scalable textual analysis. Integrated MLflow and Weights & Biases to track experiments and artifacts, improving reproducibility and systematic model evaluation. Implemented Silhouette Score-based monitoring to track clustering performance and maintain workflow reliability.

Education

Bachelor of Technology, Computer Engineering, Vellore Institute of Technology

Bachelor of Technology - BTech, Artificial Intelligence, Resume & CV