AI & ML student with hands-on experience building on-device LLM systems and agentic pipelines from scratch. Internship background in ML deployment and data engineering. Projects range from edge AI on Android hardware to real-time computer vision — built independently, outside coursework.
- Built and integrated AI-driven modules into the core product stack, cutting manual intervention across two key production workflows.
- Designed model deployment and orchestration flows within production-grade pipelines.
- Engineered end-to-end data pipelines and feature workflows on large-scale structured datasets.
- Delivered analytics models benchmarked against business KPIs; findings adopted in BI reporting.
- Built end-to-end NLP emotion classification system on a 68,000-row real-world text corpus, achieving 92% model accuracy.
- Deployed trained model to a live web interface via REST API; integrated into client-facing product.
- Ran full EDA and feature engineering pipeline (Pandas, NumPy); results shipped in client deliverables.