Vedit Agrawal

I'm a Computer Science undergrad at BMS College of Engineering (GPA 8.6/10), and I build end-to-end ML systems — from data pipelines through to production deployment, not just notebooks and prototypes.

As an Applied AI Intern at ScaleDux, I designed ML pipelines in Python, scikit-learn, and XGBoost that lifted model accuracy by 18%, built and containerized computer vision models with TensorFlow and YOLOv8, deployed services on AWS EC2/Lambda with CI/CD, and built asynchronous pipelines (Celery + Redis) that cut API latency by 40% — all monitored through Prometheus and Grafana.

My project work spans BERT/USE-based resume matching (CareerConnect AI), LSTM-based stock forecasting (85% directional accuracy), and hands-on work with RAG, LangChain, and agentic AI workflows. I move comfortably across the stack — Node.js/FastAPI backends, React frontends, SQL/NoSQL/vector databases (PostgreSQL, MongoDB, FAISS, Pinecone), and cloud infrastructure (AWS, GCP, Oracle Cloud, Docker, Kubernetes) — and I hold OCI certifications in Data Science and Generative AI.

I'm currently open to Software Engineer, Machine Learning Engineer, Data Engineer, and Applied AI internship/remote roles — anywhere I can own a system end-to-end, from data to deployment. Happy to talk backend architecture, ML infrastructure, or data pipelines — reach out anytime.