Sai Rajesh Rapelli

3+ years of work experience

Mar 20268 mosPresent

– Develop, deploy, and optimize AI/ML models for waste segregation, garbage surveillance, and route planning. – Build and manage scalable data pipelines, MLOps workflows, and analytics platforms. – Oversee the complete model lifecycle including data acquisition, feature engineering, training, deployment, monitoring, and retraining. – Participate in prototyping, on-site testing, and performance trials to ensure reliability and robustness. – Collaborate cross-functionally with engineers, customers, investors, and ecosystem partners to drive product innovation.

Jan 20242 yrsDec 2025

Project: Navy Obsolescence Prediction (Post-Graduate contract extension) Technologies: Azure Data Factory, Azure Data Lake Storage Gen2, Databricks, Python, SQL – Built Azure Data Factory pipelines using Copy Data, Lookup, and ForEach activities to ingest high-frequency sensor and voltage data from source systems into Azure Data Lake Storage Gen2. – Implemented medallion architecture pipelines using Delta Lake to support analytics and downstream machine learning workflows. – Automated recurring transformation workflows using scheduled triggers, reducing manual data preparation effort by 50%. Project: CO2 Emission Forecasting Pipeline Technologies: Azure Data Factory, Databricks, Python, Deep Learning – Designed end-to-end ETL pipelines in Azure Data Factory to ingest, clean, and transform high-frequency time-series sensor data. – Developed PySpark preprocessing workflows involving feature selection, time-series sequence preparation, data reshaping, and output transformation to support LSTM-based modeling. – Assisted with monitoring ADF pipeline runs and alerts, helping ensure timely ingestion of sensor data for analytics. – Published peer-reviewed research titled “A LSTM with Dual-stage Attention Method to Predict Amine Emissions for Carbon Dioxide Capture and Storage”.

Sep 20211 yr 5 mosJan 2023

Project: WEC Energy Resource Management and Incident Reporting Platform Technologies: Excel, SQL, MS SQL Server, Power BI – Used MS SQL Server queries to track and report daily incident counts and operational metrics for the team. – Maintained resource, incident, and audit data in Azure Data Lake Storage, organizing data into raw and curated layers. – Supported data ingestion by moving on-premises operational and incident data into Azure Blob Storage. – Supported ETL workflows and prepared data for Power BI and Excel dashboards, updating metrics in Accenture internal tools (MyWizard and SMART).