Jigyansh Kumar

Built predictive forecasting models achieving 85%+ accuracy and implemented NLP classification for 10K+ daily documents, showcasing proficiency in managing and processing large-scale content.

Deployed

FastAPI endpoints serving ML models with 99% uptime and optimized processing pipelines for 50K+ records, reducing latency by 60%—critical for production-grade systems. Collaborated with cross-functional teams to deploy 3+ ML models to production, improving business KPIs by 35%, demonstrating effective teamwork in coordinating technical and creative goals.

AI/ML Engineer - TPMGuru Pvt. Ltd.

(2025-01)

• Built predictive forecasting models achieving 85%+ accuracy and implemented NLP classification for 10K+ daily documents, showcasing proficiency in managing and processing large-scale content.
• Deployed FastAPI endpoints serving ML models with 99% uptime and optimized processing pipelines for 50K+ records, reducing latency by 60%—critical for production-grade systems.
• Collaborated with cross-functional teams to deploy 3+ ML models to production, improving business KPIs by 35%, demonstrating effective teamwork in coordinating technical and creative goals.

ML Research Intern - LetsGrowMore

(2024-06 - 2024-12)

• Built data preprocessing pipelines with Pandas and NumPy, improving model performance by 15% and created 5+ reporting dashboards, reducing reporting overhead by 30%.

AI/ML Trainee - CodeGoTrendz

(2024-01 - 2024-05)

• Built 3+ full-stack AI applications integrating ML models with React.js and Node.js frontends, delivering production features that improved user engagement by 35%.

B.Tech. - Computer Science & Engineering (AI/ML) - J.C. Bose University of Science and Technology (YMCA), Faridabad (2022 - 2026)