KUMAR SAHIL

Led the development and optimization of a scalable social media analytics platform, significantly enhancing user engagement and system efficiency.

• Architected and developed a scalable social media analytics platform using Java and SQL, driving a 60% increase in user engagement.
• Optimized over 30 reusable backend components, enhancing data processing efficiency by 120%.
• Coordinated and led a 3-developer team, accelerating feature delivery speed by 30% through streamlined release processes.
• Integrated critical APIs and established robust CI/CD pipelines, resulting in a 20% reduction in deployment time.

Automated content analysis and developed social media metrics tools to enhance online readership and optimize article strategy.

• Automated content analysis workflows, which contributed to a 25% increase in online readership.
• Developed and implemented social media metrics tools, successfully boosting user engagement by 15%.
• Utilized data analytics to refine article strategy, leading to a 20% improvement in engagement metrics.

Developed an AI-powered medical chatbot capable of disease prediction, image processing for medical diagnostics, and appointment booking.

Developed a full-stack bookmarking application with real-time user authentication and AI-powered summarization features.

Designed and developed an intelligent fall detection system with high accuracy, featuring rapid activation and a published research paper.

Built a predictive model to analyze the impact of ad spending on election outcomes, processing large datasets and generating insightful visualizations.

Achieved a LeetCode Contest Rating of 1400, placing in the Top 8000 globally, having successfully solved over 500 programming problems.

Secured a CodeChef Rank of 1263 out of 30000 participants, demonstrating strong competitive programming skills.

Represented at the national level as a Kabaddi player, demonstrating leadership, teamwork, and discipline.

Recognized as a finalist in the Smart India Hackathon, a national-level competition for innovative solutions.

Achieved finalist status (Top 17 out of 50,000+ participants) in the TVS Credit EPIC competition, showcasing exceptional problem-solving and innovation.

Authored and published a research paper in IJIRT detailing the design and performance of an intelligent fall detection system achieving 99% accuracy.