Computer Science .Net Core

Ram Abhishek Ramadoss Sivadoss

602-***-**** ***************@*****.*** LinkedIn GitHub

EDUCATION

Master of Science, Computer Science Expected December 2024 Arizona State University, Tempe, Arizona GPA: 3.67/4.00

Bachelor of Engineering, Computer Science and Engineering May 2020

Easwari Engineering College, India CGPA: 8.54/10.0

TECHNICAL SKILLS

•Dynamics 365 E-Commerce, CRM, BC, ERP, MVC, C#, X++, .NET Core & Framework, HTML, Typescript, SQL Server, SSMS

•Full stack development incorporating APIs’, Postman, IIS, ILSpy, DotPeek, and pipeline handling using Lifecycle Services (LCS)

•Selenium, MSOffice, Power Apps, REST, ReactJS, AngularJS, NodeJS, Agile Methodologies, Azure DevOps, Payment gateways, Data Analytics, SAP, CosmosDB, MongoDB, Python, JavaScript, C/C++, and Java and WebSec protocols

•Postgres, Machine Learning, Data analysis & visualization tools & libraries, TensorFlow, Tableau, SciPy, Scikit, and Matplotlib

•PowerShell, Windows Server, GitLab, Robotic Process Automation tools, Automation Anywhere, UiPath, CUDA, and Unity

•Certifications: MB300, MB400, PL900 and AZ900

PROFESSIONAL EXPERIENCE

Microsoft: Technical Consultant 10/2021 – 05/2023

Empowered a client with 2800 stores and online commerce, delivering agile solutions using D365 Commerce Retail & AX with corresponding documentation (TDD), providing rapid responses to emergent requirements. Resolved critical production issues with precision and implemented measures for continuous improvement by brainstorming with the client directly and testing

•Delivered four critical functionalities using Dynamics 365 Commerce, CRM, Power Apps, and Business Central & supported design implementations, package deployments, and pipeline management through Azure DevOps & Lifecycle Services (LCS)

•Developed an API-based payment gateway for card & check transactions to eliminate hardware pairing and setup concerns

•Created an Available to Purchase Cache system using Azure functions for retrieving and updating real-time ATP quantities across all stores for inventory lookup, delivery, pickup, and Final ATP check, for purchase, transfer, or return orders

Accenture: Packaged Application Development Senior Analyst 11/2020 – 08/2023

Innovatively addressed Point of Sale requirements of nine clients as full stack developer in Dynamics 365 Commerce, providing cloud-based and application-based solutions. The role also included evaluation, testing, code reviews, & pipeline management

•Executed end-to-end development, including building application pages (HTML/Typescript/X++), utilizing Git, Azure, LCS, CRM, BC, and DevOps for seamless pipeline handling & deployment to ensure efficient database communication (C# & SQL)

•Orchestrated and directed nine vital projects, corroborating seamless team collaboration, and conducted rigorous testing sessions to validate & enhance submitted work, resulting in around 20-25% decrease in post-release bugs reported

•Promoted to senior analyst within one year and selected as team lead for four of the nine projects, responsible for team guidance, including code reviews and advice to the team for priority requirements and issues

•Achieved “Ace” award twice and “Sparkling Star” award once, for handling six critical clients

Accenture, India: Software Development Intern 01/2020 – 04/2020

Rapidly attained proficiency in robotic process automation tools, Automation Anywhere, & UiPath, through professional courses, demonstrating swift adaptation and skill application to deliver inventive solutions for automating recurrent tasks

ACADEMIC/RESEARCH PROJECTS

Blending Graph U-nets and Graph Neural Diffusion for Node Classification 08/2023 – 12/2023

•Successfully initiated and led a research project in collaboration with the professor, integrating Graph U-nets and GRAND to demonstrate precise GNN classification, achieving an increase in accuracy for Cora and Cite seer datasets using Python

•Acquired knowledge on how GNN and GRAND work to merge them, significantly enhancing U-nets' effectiveness by

1 - 2% for GNN predictions using Neural diffusion which incorporates continuous data prediction rather than discrete

Banknote Recognition for Smart Transactions 09/2019 – 03/2020

Established a project idea managing video-to-speech conversion and banknote recognition with 88% accuracy, facilitating easy transactions for visually challenged people using TensorFlow, by training a CNN, working on tensors to aid seamless image recognition, publishing a paper & delivering a prototype of solution to college for further research and development

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