CI/CD Pipeline Implementation for AWS Application Deployment via GitHub Actions, Selenium, Docker and Ansible• Implemented the CI/CD pipeline by leveraging GitHub Actions, Maven, and Selenium, reducing build time by 30% and increasing deployment efficiency, thereby accelerating time-to-market for Java applications. • Introduced common branching strategies, automated build/integration checks, and simplified deployment• Successfully advanced the pipeline to Docker image creation and publication on Docker Hub.• Utilized SQL Server for data storage and retrieval within the applicationAdvanced Traffic Management Edge Device Development• Developed a Python-based edge device utilizing Computer Vision and Machine Learning, achieving 96% accuracy in real-time traffic analysis, revolutionizing traffic management strategies.• Implemented secure data transmission and storage in AWS DynamoDB via AWS IoT and AWS Lambda, ensuring data integrity and compliance with security standards.• Established a robust CI/CD pipeline with AWS CodePipeline for automated building and deployment, facilitating seamless updates and enhancements.• Managed fleet of edge devices, coordinating rolling updates through AWS IoT Jobs, ensuring uninterrupted system functionality.• Conducted essential maintenance tasks using Bash scripting, including updates, patches, and security fixes, to uphold system reliability and security.Multi-environment Microservices Deployment on AWS via Terraform, Ansible and EKS• Orchestrated the deployment of 10 microservices across development (dev) and production (prob) environments, achieving a notable 60% reduction in deployment errors.• Developed CI/CD pipeline, Kubernetes clusters, and cloud infrastructure, resulting in a significant 60% improvement in efficiency, performance, and security across the website.• Resulted in the delivery of approximately 6 features per month, enhancing overall project output and efficiency.

• Engineered and launched live video streaming from Raspberry Pi camera to Camlytics platform, enabling real-time traffic analysis. The system accurately captured vehicle count, time-headway, space-time trajectories, and average speed metrics. • Enabled seamless data transmission of sensor data to cloud-based storage and analysis platforms by integrating GSM SIM900 modules with Arduino and Raspberry Pi, resulting in increased real-time data availability for analysis and decision-making. • Developed a bottleneck detection in-vehicle system through the development of a cloud-based system for vehicle behavior analysis utilizing the OBD-II system, leading to the detection of bottlenecks on the road.