Experience

💼 NetoAI

SDE-1 Intern (AI/ML & LLM)
Jan 2026 - Present · 1 mo · Remote

• Working as an SDE-1 Intern specializing in AI/ML and Large Language Models.
• Contributing to cutting-edge AI solutions and production-grade systems.
• Developing and deploying AI/ML models and LLM-based applications.

🏥 DICOM Automation Service (Under Prof. Dr. Deepak Agrawal, AIIMS Delhi)

Research Collaborator Jul 2025 – Sep 2025 · New Delhi, India

• Architected and deployed an end-to-end video-to-DICOM pipeline using Python, PostgreSQL, and Dockerized microservices integrated with Orthanc PACS.
• Achieved 95% workflow automation and near-real-time processing (approx 30s per video), significantly improving medical-image processing efficiency and clinical readiness.
• Completed the research project which was recognized as a significant step toward improving medical-image processing within AIIMS’ digital-health research environment.

💻 IBM SkillsBuild | Agentic AI: From Learner to Builder

AI Agent Architect Training Program Jul 2025 – Aug 2025 · Remote

• Completed a 4-week intensive training program on Agentic AI and AI Agent Architecture with the CSRBOX Foundation.
• Gained hands-on experience in building and implementing production-ready AI agents using the Gemini API.

🔬 GGSIPU – University School of Automation and Robotics

Undergraduate Research Associate
Jul 2024 – Jun 2025 · New Delhi, India
Mentors: Dr. Sanjay Kumar Singh (GGSIPU), Dr. Pranshu Saxena (Bennett University)

• Conducted deep learning research in medical and agricultural imaging.
• Published 2 IEEE papers focused on transfer learning and Bayesian optimization.
• Achieved 97.23% accuracy in tomato leaf disease detection using optimized Xception.
• Enhanced breast tumor classification to 98.46% using fine-tuned CNNs and Population-Based Training.
• Currently leading a journal-level study involving MC Dropout and temperature scaling for calibrated predictions.

📊 M.K. Associates (Remote)

Financial Data Analyst Intern
Jan 2024 – Jun 2024

• Performed EDA on 10K+ financial transactions across 700+ client accounts.
• Identified and flagged 150+ high-risk anomalies, improving audit workflows.
• Built ML models for financial risk classification (93% accuracy).
• Automated reporting in Excel, reducing manual effort by 40%.
• Supported regulatory compliance by aligning outputs with business policies.