Pavan Kumar Adapala

- Conducted an in-depth analytics study on forecasting energy consumption in industrial paint shops - Designed and evaluated ML-based forecasting models (batch and one-step-ahead) on IIoT time series data for industrial energy optimization, achieving 95% predictive performance - Improved model robustness by 15% through hyperparameter fine-tuning - Developed forecasting approaches with potential to reduce energy costs and enhance sustainability in MES-driven industrial paint shops

- Designed and optimized CI-CD-CT Jenkins pipelines to enhance the SDLC of autonomous parking software, including 2nd-Level-Support for build and integration issues - Implemented data-driven automation strategies using Python and REST APIs to parse CI/CD build artifacts and automatically email key test summaries (unit, smoke, integration), reducing debugging time by 30% - Built a centralized Grafana dashboard to monitor CI/CD build KPIs across multiple projects, enabling data-driven release planning

- Provisioned and managed AWS infrastructure using Infrastructure as Code (IaC)-Terraform and CloudFormation to support development and operations teams, ensuring scalable, repeatable deployments - Developed custom CloudWatch dashboards and alerting systems by integrating open-source monitoring agents, enabling proactive application management, and reducing downtime - Documented troubleshooting procedures in Confluence, enabling faster resolution of recurring issues and promoting team knowledge sharing

- Deployed the web application following 3-tier AWS architecture, ensuring secure, highly available, and fault-tolerant systems - Contributed to cloud migration proof-of-concept project, reducing infrastructure costs by 25% through implementation of Auto Scaling, production-ready cloud infrastructure - Established GitHub repository standards including branch protection rules, mandatory CI checks before merges, improving code quality and reducing integration issues across the team