Abhishek Manyam

Master's

in computer science

from Oregon State

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Abhishek Manyam

Full Stack Engineer

I primarily focus on TypeScript-based full-stack projects (React, Angular, Firebase) and cloud platforms, with expertise in PostgreSQL and cybersecurity.

MakeitMVP

Software developer Intern

at MakeItMvp

redvike

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ARIZONA, USA

  1. September 2024 — Present

    Architected and developed a full-stack event management platform using React and Firebase, implementing role-based access control for event organizers and participants. Integrated Stripe payment gateway for subscription management and event registration, streamlining monetization workflows. Designed and implemented scalable microservices architecture using Firebase Cloud Functions, improving system modularity and maintainability.

    • React
    • Firebase
    • Cloud Functions
    • Stripe API
    • TypeScript
    • Node.js
    • Docker
    • Kubernetes
    • GitHub
    • Confluence
    • JIRA
  2. August 2023 — February 2024

    Developed and maintained microservices using Java Spring Boot and core Java concepts, achieving 99.9% system uptime. Designed and implemented RESTful APIs with Spring Boot, integrating with MongoDB and Redis for optimized data persistence and caching, resulting in 40% improved response times. Established CI/CD pipelines using Jenkins and GitHub Actions, implementing test-driven development practices that reduced production bugs by 60%.

    • Java
    • Angular
    • TypeScript
    • Python
    • FastAPI
    • Docker
    • AWS EC2
    • PostgreSQL
    • NoSQL
    • REST APIs
    • JWT
  3. June 2021 — July 2022

    Managed access controls for 1,500+ users through Active Directory and Sailpoint, maintaining zero security incidents. Developed PowerShell automation scripts that reduced manual access management tasks by 80%. Achieved 98% first-call resolution rate managing 400+ monthly ServiceNow tickets while ensuring SOX/SOC1/SOC2 compliance.

    • Active Directory
    • Sailpoint
    • PowerShell
    • ServiceNow
    • Microsoft O365
  4. May 2019 — July 2019

    Developed a face detection and recognition system using Multi-task and ResNet neural networks, achieving 95% accuracy. Optimized model deployment on NVIDIA Jetson TX1, reducing processing time by 20%. Implemented real-time processing capabilities that improved system performance by 30%.

    • TensorFlow
    • Keras
    • Python
    • NVIDIA Jetson TX1