RUDRAKSH SACHDEVA
Tech Computer Science and Engineering student with focused interests in Machine Learning, Artificial Intelligence, and Full-Stack Web Development.
About
I’m Rudraksh, a B.Tech Computer Science and Engineering student with focused interests in Machine Learning, Artificial Intelligence, and Full-Stack Web Development. I actively work on designing and implementing scalable software solutions, applying strong fundamentals in data structures, algorithms, and system-level problem solving.
I have participated in 7+ national-level hackathons, consistently reaching the final rounds, and I’m a national-level hackathon winner, where my team developed a high-impact technical solution evaluated among top teams nationwide. These experiences enhanced my ability to analyze problem statements, architect solutions, collaborate in fast-paced environments, and deliver production-ready prototypes under tight timelines.
I have gained practical exposure through open-source contributions and industry internships, strengthening my skills in end-to-end application development, model integration, and real-world software engineering practices.
I am continuously improving my expertise in AI-driven systems, backend development, and modern web technologies, and I am keen to contribute to challenging engineering problems and impactful technology products.
I enjoy clean code, solving real-world problems, and collaborating in agile teams. I’m actively seeking roles in software development, machine learning, or web technologies.
- 1 year's experience
- Joined February 2026
Industry experience
- Software & Internet
- Education
- Professional Services
Work experience
Contributed to backend development and automation tasks using Python for internal tooling and process optimization. Designed and implemented web-based applications with Flask, integrating RESTful APIs for seamless data exchange. Collaborated remotely using Git and GitHub for version control, adhering to agile development methodologies. Applied software engineering best practices including modular design, code readability, and continuous integration principles.
Worked on end-to-end machine learning pipelines involving data collection, preprocessing, model building, and evaluation. Applied Python libraries such as Pandas, NumPy, scikit-learn, and Matplotlib for data cleaning, feature engineering, model training, and visualization. Developed scalable ML workflows using supervised learning algorithms and contributed to performance tuning and testing in a collaborative, remote environment.
Education
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