• Led the end-to-end migration to version control and automated deployments for Ecomm surveillance and Enterprise Archival solutions, utilizing Jenkins, and Ansible to streamline processes.• Developed custom automation scripts using bash, playbooks, and ansible templates to seamlessly integrate testing and deployment stages, leading to a 40% reduction in deployment time and a 50% increase in deployment frequency.• Developed extensive test suites covering unit, integration, and end-to-end scenarios leveraging PyTest. Integrated tests into continuous integration pipelines for rapid feedback and defect detection.

• Designed and Implemented End-to-End Data Ingestion Pipeline for Ecomms and AComms for compliance reporting.• Deployed a modular pipeline architecture using Apache Airflow for seamless handling of varying data volumes from 21 EComm sources. Implemented fault-tolerant data processing techniques in Hadoop using Apache Spark, ensuring data integrity and reliability.• Analyzed query execution plans using database profiling tools like MySQL Workbench, identifying performance bottlenecks and inefficiencies.• Applied optimization techniques such as index tuning and query plan analysis using MySQL, resulting in a 30% improvement in query performance.

• Engineered a Python-based health monitoring application within the ServiceNow platform. Made use of the Django framework for creating RESTful APIs to interact with ServiceNow.• Automated asset discovery and tracking by seamlessly integrating Python scripts with ServiceNow's Configuration Management Database (CMDB), streamlining asset management processes.

• Investigated non-linear behavior in complex neural networks, proposing novel modeling techniques to enhance performance by addressing non-linearity.• Utilized non-linear datasets and advanced classification techniques, improving the accuracy and efficiency of neural network models in pattern recognition and data analysis.• Developed a solution to handle non-linearity within neural network architectures, contributing to the advancement of understanding and effective modeling of complex data distributions.