Bhuvesh Singh

• Proficient in working with Java, Spring Boot, MVC Architecture, REST API, Software Development Life Cycle (SDLC), Agile methodology, Spring Frameworks.
• Payment web applications are developed in accordance with PCi-DSS and PCA-DSS guidelines which handles 10,00,000 request every second.
• Writing Jenkins Scripts for Building and Deploying applications on the cloud and optimizing the running time of scripts to the maximum of 21 seconds.
• Used Dockers wherever applicable in case of creation of images and loading on server.
• Implemented security patterns like JWT to enhance encryption and protect against vulnerabilities.
• Practiced TDD(Test Driven Deployment) by writing unit test cases using JUnit.
• Bespoke development from scratch.
• Additional new delta changes incorporation done as per timelines shared by customer post sprint review and product backlog / SIT & UAT feedback.
• Successful ans smooth transition from Test environment to Production environment.
• Direct interaction with customer SPOC for gathering the requirements, documenting and freezing the scope with mutual consensus considering the complexity, feasibility, risk and timelines.
• Cyber Security Remediation(Application generated Risk)- Worked on Analysis of server and database related vulnerabilities like upgrading Oracle and Java patches on the Application Servers, Analysis of network Security protocols used by applications like TLS(ciphers) vulnerability and remediating it.
• Managed and analyzed weekly team efficiency reports using ServiceNow Snow portal and Excel for project oversight and optimization.
• Delivered Knowledge transfer sessions on Java, Spring Boot, Gradle, and CI/CD to the newly onboarded members within the organization.
• Contributed in an Agile programming environment, defining user stories, estimating tasks, and implementing interventions.

Developed Machine Learning Projects with Python and OpenCv :

• Facial Recognition Security System: It is a machine learning application that grants access to authorized person after authenticating person’s Face and Facial Markings. It's KNN model is pre-trained with 75,000 images of different human beings.
• Object Detection: Machine learning application developed using python libraries and machine learning models that detects types of objects like table , chair , glass etc.
• Vehicle Detection: Machine learning application that detects types of vehicles like Cars, Bicycle, Motorcycle and Trucks.