M.S. Computer Science Graduate

Hi, I'm Priyanka

Software engineer building production-oriented software, AI/ML systems, cloud infrastructure, computer vision applications, and distributed systems. Experienced with Python, Go, Java, Azure, Docker, REST APIs, CI/CD, and modern machine learning workflows.

View Resume

Software • AI • Cloud

Go + Python

Azure + Docker

AI/ML

About Me

Engineering software across AI, cloud, and distributed systems.

My Journey

My path from civil engineering into computer science shaped how I approach technical problems: understand the system, identify the bottleneck, and build something measurable and reliable.

I completed my M.S. in Computer Science at Texas Tech University in May 2026. My work spans software engineering, reinforcement learning, machine learning, computer vision, cloud infrastructure, REST APIs, CI/CD, and distributed systems.

Education

M.S. Computer Science

Texas Tech University

Graduated May 2026

Focus Areas

Software + AI + Cloud

Distributed systems, ML, computer vision, APIs, infrastructure

Availability

Open to opportunities

Software, AI/ML, cloud, and data engineering roles

Beyond Tech

🏸 Badminton 🎵 Classical Music 🎸 Sitar

Technical Skills

Languages

Python Go Java C++ C# JavaScript SQL

AI / ML

PyTorch TensorFlow LLM Applications Reinforcement Learning Computer Vision Model Evaluation

Cloud / DevOps

Azure Docker Kubernetes / AKS Terraform CI/CD GitHub Actions

Backend / Data

REST APIs Flask Spring Boot PostgreSQL SQL / NoSQL

Systems

Linux Distributed Systems Networking Git / GitHub Debugging

Engineering Practices

Unit Testing API Design Automation Root-Cause Analysis

Professional Experience

Graduate Assistant — Technology Support & Customer Experience

Texas Tech University

2024 – 2026

Texas, USA

▶ Streamlined enterprise software deployment workflows using KACE across 300+ university-managed systems, reducing setup time by 70%.
▶ Diagnosed and resolved 500+ hardware, software, and system issues while improving troubleshooting efficiency and reducing recurring support problems.
▶ Supported remote administration, device configuration, root-cause analysis, and high-volume technical support for students, faculty, and staff.

Cloud Engineering Intern

TopHat Software

Jun 2022 – Jul 2023

Remote

▶ Built and maintained Azure infrastructure with Terraform/ARM, improving platform uptime from 88% to 98.5%.
▶ Containerized workloads with Docker and AKS and supported Azure DevOps CI/CD pipelines for repeatable deployments.
▶ Automated cloud and operational workflows with Python/PowerShell while improving incident response, reliability, and cloud cost efficiency.

Featured Projects

Selected work across distributed systems, computer vision, generative AI, full-stack AI, and machine learning.

Distributed Systems

ChainForge

v1.0.0

Distributed blockchain network built from scratch in Go with Proof-of-Work mining, ECDSA-signed transactions, persistent bbolt storage, REST APIs, peer synchronization, automatic block propagation, Docker Compose, and GitHub Actions CI.

Go Docker REST APIs ECDSA SHA-256 bbolt

View on GitHub ↗

Computer Vision / AR

VisionAR

Real-time marker-based augmented reality system using ORB feature matching, RANSAC homography, Kalman-filter tracking, camera projection, OBJ model rendering, automated tests, and GitHub Actions CI.

Python OpenCV NumPy ORB RANSAC Kalman Filter

View on GitHub ↗

Generative AI

DiffusionLab — DDPM From Scratch

Implemented a denoising diffusion probabilistic model in PyTorch with a custom UNet training pipeline, configurable training arguments, checkpointing, sampling workflows, and loss visualization.

Python PyTorch UNet Diffusion Models Deep Learning

View on GitHub ↗

Full-Stack AI

AI-Driven Quiz Platform

Built an AI-powered learning platform that generates quizzes from lecture content, integrates Gemini with Flask/PHP workflows, and supports student progress tracking and automated assessment.

Flask Gemini PHP REST APIs Full Stack

Research & Graduate Work

M.S. Thesis

Dual-Track Generation for Code-to-Documentation

Graduate thesis focused on improving code-to-documentation generation through a dual-track framework that combines complementary generation and verification signals to produce more accurate, consistent, and useful technical documentation from source code.

Code-to-Documentation CodeT5 NLP Generation Evaluation BLEU / ROUGE

Degree

M.S. Computer Science

Institution

Texas Tech University

Completed

May 2026

Let's Connect

I'm open to software engineering, AI/ML, cloud, and data engineering opportunities.

✉️

prisaxen@ttu.edu

💼

Connect professionally

💻

GitHub

github.com/saxenap2804