I'm Dyuti Mengji.
Software Engineer | Full Stack Developer |
Traveller | Artist
I am a results-driven Software Engineer and Computer Science graduate student at Arizona State University, where I also serve as a Graduate Service Assistant, mentoring students and applying my expertise in C++, assembly, and systems programming. Before ASU, I earned my Bachelor’s in Computer Science and Engineering from Gokaraju Rangaraju Institute of Engineering and Technology, graduating with distinction. My journey in technology has been shaped by impactful academic and professional experiences—ranging from developing scalable serverless pipelines on AWS to engineering robust data scraping solutions and building intuitive web applications with the MERN stack. As a Software Engineer Intern at Dark Alpha Capital LLC, I built scalable data pipelines, internal platforms, and modern GUIs. At Techmirus Innovations Pvt. Ltd., I contributed as a Full Stack Development Intern, designing responsive, secure applications and collaborating in Agile teams to deliver impactful solutions. With strong skills in Python, C++, JavaScript, React, Next.js, Node.js, and AWS, I thrive on transforming complex challenges into elegant solutions. Passionate about problem-solving and continuous learning, I am driven to push the boundaries of technology and create meaningful impact.
C++
Python
JavaScript
TypeScript
HTML
CSS
SQL
Java
ReactJS
Next.js
Node.js
Kafka
JUnit
Redux
MySQL
MongoDB
PostgreSQL
AWS
GCP
Git
GitHub
Docker
Selenium
Postman
Linux
JIRA
Figma
Master of Science
Computer Science
Arizona State University
Bachelor of Technology
Computer Science and Engineering
Gokaraju Rangaraju Institue of Engineering and Technology
High School
FIITJEE
Graduate Service Assistant (CSE 100, CSE 230, CSE 325)
- Assessed 500+ student submissions in C++, assembly, and embedded systems, ensuring accuracy and code efficiency
- Applied structured grading rubrics to maintain 100% consistency across programming assignments, lab reports, and exams
- Monitored academic platforms for code plagiarism and unauthorized content sharing using automated detection tools
Software Engineer Intern
- Developed an internal ticketing system with CRUD, threaded replies, view counts, and upvote-based usefulness tracking
- Built 25+ robust scrapers using Selenium, BeautifulSoup, and Pandas to automate data collection from 567+ web pages
- Engineered a modular scraping pipeline with Excel I/O and structured exports in multiple formats
- Compared Gemini API vs. Selenium+BeautifulSoup to optimize speed, reliability, and data quality across complex sites
- Enhanced scraper usability with a modern GUI featuring intuitive flow and professional typography
Full Stack Development Intern
- Contributed to the development of dynamic, responsive web applications using the MERN stack
- Designed scalable RESTful APIs using MongoDB, ensuring secure data transactions between frontend and backend services
- Collaborated in Agile sprints using Git and CI/CD tools to streamline code deployment and enhance team productivity
- Implemented responsive design principles using React and Tailwind CSS, achieving high performance across devices
Designed and implemented a scalable, serverless face recognition pipeline using AWS Lambda, Docker, and ECR. Deployed containerized Lambda functions for face detection and recognition with PyTorch models, integrated SQS for asynchronous processing, and optimized Docker images for low latency. Achieved 100% success rate with <2s average latency on 100 inference requests.
Developed a full-stack, scalable face recognition web app using AWS IaaS services. Implemented a Python-based HTTP server on EC2 for image classification, S3 for storage, SQS for queuing, and SimpleDB for lookups. Integrated PyTorch models for real-time recognition and deployed an autoscaler managing 15 EC2 instances with <1.0s response latency for 100 concurrent requests.
A full-stack travel application inspired by Airbnb that lets users discover, create, edit, and review travel listings. Features include authentication, reviews, Cloudinary-powered image uploads, and secure CRUD operations for listings.
Independently designed and developed a full-stack, responsive website for improved online presence and patient engagement. Built the frontend with React and Tailwind CSS, ensuring seamless navigation and cross-browser support. Developed RESTful APIs with Node.js and Express.js to handle secure patient appointments and inquiries.
Performed sentiment classification on Amazon reviews using a Random Multimodel Deep Learning (RMDL) architecture with DNN, CNN, and RNN models. Incorporated BERT tokenization and aspect term extraction for preprocessing. Developed a novel Coot-Political Algorithm (CPA), a hybrid optimization technique combining COOT and Political Optimizer, to enhance weight optimization.
Implemented a CNN-based system to improve accuracy in detecting offline signature forgeries. Extracted features such as ratio, centroid, eccentricity, and skewness, and developed a Tkinter-based GUI application to visualize preprocessing stages and final classification. The system successfully identifies genuine vs forged signatures.
Contact
I'd love to hear from you!
Whether it’s an idea, opportunity, or just a friendly hello, I’d be glad to hear from you.