Shivansh Dharni

I’m a B.Tech Computer Science graduate with more than a year’s experience in Java backend engineering, where I’ve built and optimized production REST APIs and microservices. In my current role as an Assistant Software Engineer, I debug production issues through root-cause analysis, improve test coverage, and strengthen API reliability with unit and integration tests.

Alongside backend work, I build applied AI/ML systems end-to-end. I’ve developed an NLP RAG chatbot with LangChain, HuggingFace, and ChromaDB using source-cited responses, and I built a survival-analysis-based customer churn model using Random Forest and Cox PH—achieving 77.36% accuracy and 0.84 ROC-AUC.

I’m actively focused on AI Engineering, GenAI systems, and MLOps workflows, translating models into dependable services with practical tooling like MLflow and Docker. From CNN-based crop disease detection to transformer-based intent classification with sub-500ms latency, I’m motivated by engineering clarity: measurable performance, clean pipelines, and systems that hold up in production.