Vaibhav Narute

I build intelligent, production-grade AI systems that blend strong machine learning fundamentals with scalable real-world deployment. I focus on supervised learning, feature engineering, model evaluation/optimization, and turning complex business problems into high-impact AI solutions. My work spans designing and deploying Generative AI and Retrieval-Augmented Generation (RAG) pipelines with modern LLM stacks.

Recently, as an AI/ML Engineer Intern, I architected and deployed a production MIS microservice on Google Cloud Platform, reducing manual operational overhead by approximately 70–80% while improving internal reliability. I also engineered automated assessment pipelines for test generation, scheduling, evaluation, and analytics—cutting manual coordination effort by 65% and stabilizing Sev-1 production issues.

Across projects, I’ve built multimodal and retrieval-first systems (AutoRAG), churn prediction with robust generalization (ChurnGenie), and an end-to-end AI hiring platform (Resumate). I deliver with MLOps workflows (Airflow + MLflow), retrieval optimization (Pinecone, embedding hyperparameter tuning), and production frameworks using APIs and streaming pipelines.