Chandu Priya

Designed and deployed end-to-end ML pipelines for data ingestion, preprocessing, feature engineering, model training, and production deployment; built predictive models for classification, regression, clustering, and recommendation systems; developed deep learning solutions for NLP and computer vision; conducted advanced statistical analysis, hypothesis testing, and A/B experimentation; implemented MLOps with CI/CD, MLflow, Docker, and Kubernetes to streamline experimentation and scalable deployments; engineered large-scale data solutions using Spark, Hadoop, and cloud platforms; developed time-series forecasting models; applied transformer-based NLP models for document classification and entity extraction; leveraged graph-based ML for various use cases; created interactive dashboards to communicate insights to stakeholders.

Developed predictive and prescriptive models for classification, regression, clustering, and recommendation; applied reinforcement learning and advanced optimization techniques for automation and intelligent decision-making; implemented MLOps workflows with CI/CD, Docker, Kubernetes, Airflow, and MLflow; conducted hyperparameter tuning and cross-validation to enhance model performance; designed scalable recommendation engines and NLP/CV pipelines; optimized training costs via distributed training and cloud resources; automated retraining pipelines and model monitoring; collaborated with product, engineering, and analytics teams to integrate AI into customer-facing applications.

Collected, cleaned, and processed large datasets; performed EDA to identify trends and actionable insights; built predictive and descriptive models; developed time-series forecasting for demand and resource planning; created interactive dashboards; designed recommendation systems; applied advanced NLP techniques for unstructured data; built end-to-end data pipelines for ingestion, transformation, and deployment; performed clustering for customer segmentation; automated reporting; utilized cloud-native tools for scalable training and deployment.