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I’m a Computer Science graduate passionate about building secure and intelligent software. My work spans software development, DevOps, and AI systems with a strong focus on performance, security, and reliability.
I care about writing clean code, automating processes, and creating systems that scale with confidence.
Hi! I'm Vivek Raj, a Computer Science graduate with a focus on Artificial Intelligence from Binghamton University. I’m passionate about building secure, intelligent systems that solve real-world problems.
My journey began during my undergraduate studies at Binghamton, where I developed a strong foundation in algorithms, systems, and AI. That foundation grew into a deeper interest in cybersecurity, machine learning, and system design during my master’s.
I’ve contributed to Apple’s open-source FoundationDB, competed in global security competitions like MITRE’s eCTF, and conducted research in prompt engineering for NLP. I also mentored over 100 students as a Teaching Assistant in Computer Security and Algorithm Design.
These days, I love crafting creative AI solutions, optimizing system performance, and working on projects that sit at the intersection of code, security, and innovation.
HUGGING FACE
PYTORCH
TAILWIND
REACTJS
NODEJS
GITHUB
GIT
C
C++
ADOBE AI
JAVA
PYTHON
TENSORFLOW
PYTORCH
SCIKIT-LEARN
LINUX
HTML
CSS
MONGODB
FLASK
C#
.NET
EMBEDDED
Binghamton University, NY | Jan 2024 – May 2025
Specialized in Artificial Intelligence, GPA: 3.94
Graduated with Summa Cum Laude honors 🏆
Binghamton University, NY | Aug 2021 – Dec 2023
GPA: 3.8 • Dean’s List
Graduated with Magna Cum Laude honors 🏆
iA (Innovation Associates), NY | Aug 2025 – Present
Enhancing and modernizing NEXIA Retail Software, a scalable pharmacy fulfillment and retail management system
supporting enterprise operations for the Defense Health Agency (DHA) and other large-scale clients.
Built and Design internal automation tools for QA analysts and software engineers to streamline debugging, testing, and
feature validation workflows, reducing manual effort by 15–20% across development cycles.
Binghamton University, NY | Aug 2024 – July 2025
Mentored 100+ students in Computer Security and Design and Algorithms, covering cryptography, network
security, data integrity, and algorithm analysis for strings, trees, graphs, and networks
Binghamton University, NY | Feb 2024 – May 2024
Analyzed and optimized prompt engineering techniques, improving AI response efficiency by up to 40% across diverse tasks.
Collaborated on developing and executing a structured validation methodology for retrieval-augmented generation (RAG)
models in NLP, improving accuracy by 15% and enhancing reliability and consistency across benchmarks.
JPMorgan Chase & Co. | July 2023
Hands-on experience with software workflows and practical dev tasks.
JPMorgan Chase & Co. | June 2023
Gained cybersecurity exposure and applied skills in simulated projects.
Central Hudson Gas & Electric, NY | Jun 2022 – Aug 2022
Enhanced SAP integration and performance with ABAP and CDS Views.
Refactored initialize_logger_level and unit_tests_version_510 in FoundationDB,
enhancing maintainability, readability, and logging efficiency. This improves debugging reliability and
makes the codebase easier to extend.
Led a 10-member team to a top-26 global finish in MITRE’s eCTF. Designed a secure TV decoder with AES-128 encryption, side-channel resistance, and anti-replay mechanisms. Captured 46+ flags by reverse-engineering adversarial firmware using C and Python.
Built AirMouse, a computer vision-based tool using TensorFlow to control a computer with hand gestures. Used a client-server model to process video and generate hand skeletons. Implemented smoothing for stable cursor control. Won Best Technical Work at Hackathon 2024
Developed a custom memory allocator in C/C++ for user-space modules, enabling dynamic memory allocation
similar to malloc() while minimizing internal fragmentation and metadata overhead.
Implemented batch-based allocation with fixed-size blocks, bitmap-based free block tracking, and first-fit
allocation.
Leveraged function interposition, optional sbrk()-based heap management, and alignment-aware block sizing
(8/16-byte boundaries) to enhance memory efficiency in real-time OS environments.
Developed a real-time data analytics dashboard integrating Hugging Face GPT-Neo (1.3B parameters) to answer
user queries across Reddit and 4chan, processing 100K+ posts and threads for large-scale textual analysis.
Implemented interactive visualizations (charts, heatmaps, histograms) using Chart.js and AJAX, enabling real-time
data exploration and insights through a responsive web interface.
Built a Python-based stock trading and forecasting tool using libraries like scikit-learn, pandas, NumPy, and PyTorch. Engineered 100+ financial features and indicators for accurate predictions. Integrated SVC, Logistic Regression, K-means, and model ensembling (Voting, Blending, Adaboost) to boost reliability. Delivered a user-friendly interface to help users make smarter trading decisions.
Led a 10-member team to secure global rank 26 and 2nd in New York State in MITRE’s eCTF competition focused on embedded system security.
Contributed to Apple’s open-source project FoundationDB on GitHub.
Awarded Summa Cum Laude distinction in Computer Science for high academic performance
Awarded Magna Cum Laude distinction in Computer Science for high academic performance
Awarded for exceptional performance in mathematics and academics.
Awarded by the National Science Foundation for GPA 3.8+.
Awarded for creating the best technical hack at the event.
Placed 2nd in prestigious hackathon hosted by YCW.
