Sarvan kondaveti

Architected unsupervised learning workflows using Python and DBSCAN to segment high-dimensional datasets and improve anomaly detection coverage. Developed production-oriented generative AI solutions with LangChain, LLM integrations, and RAG pipelines to deliver context-aware enterprise capabilities and improve response relevance. Built NLP entity recognition frameworks to transform unstructured documents into structured datasets, extracting people, companies, and domain terminology while improving automated capture. Operationalized ML workloads on Azure using Azure Machine Learning, experiment tracking, and secured inference endpoints to improve deployment reliability and shorten model release cycles. Created reusable Scikit-learn pipelines for classification and regression with feature transformations and parameter optimization, improving validation stability. Enhanced gradient-boosting solutions using XGBoost by engineering predictive features, tuning hyperparameters, and addressing

Architected unsupervised learning workflows using Python and DBSCAN to segment high-dimensional datasets and isolate irregular behavioral patterns, improving anomaly detection coverage by 24% in noisy environments. Developed production-oriented Generative AI solutions with LangChain, LLM integrations, and RAG pipelines to deliver context-aware enterprise applications, increasing relevant response generation by 32%. Built NLP entity recognition frameworks to convert unstructured documents into structured datasets, extracting people, companies, and domain terminology while increasing automated information capture by 21%. Operationalized ML workloads on Microsoft Azure using Azure Machine Learning, managed experiment tracking, and secured inference endpoints to improve deployment reliability and shorten release cycles by 27%. Created reusable Scikit-learn pipelines for classification and regression with feature transformation and parameter optimization, improving validation stability by 1

Built unsupervised learning workflows in Python using DBSCAN to segment high-dimensional data and uncover irregular behavioral patterns, improving anomaly detection coverage by 24%. Developed production-oriented Generative AI solutions with LangChain and LLM integrations, including RAG pipelines that improved relevant response generation by 32% for domain-specific enterprise use cases. Implemented NLP entity recognition pipelines to convert unstructured documents into structured datasets (people, companies, industry terminology), increasing automated information capture by 21%. Operationalized ML on Microsoft Azure with Azure Machine Learning, experiment tracking, and secured inference endpoints to improve deployment reliability and shorten model release cycles by 27%. Created reusable Scikit-learn pipelines for classification/regression with feature transformations and parameter optimization, increasing validation stability by 19%. Enhanced gradient-boosting performance using XGBoost