About
Let me introduce myself.
Hi there! I’m Riya Raut, a Computer Science grad student at Binghamton University, currently on the hunt for my moonshot 🚀 — whether it’s in software engineering, machine learning, or (ideally) somewhere perfectly in between. With 5 AI/ML internships, a white paper on GenAI, and a habit of building everything from RAG pipelines to semantic similarity tools, I’m not just job-hunting; I’m opportunity-hunting. The kind where I get to build cool things, ship fast, and learn from brilliant folks (preferably while running on ☕ and debugging at 2am like it’s a lifestyle). If you’re working on something exciting and need a fast learner who codes faster, ships with heart, and brings a human touch to AI — let’s connect! (Also, I make excellent coffee and slightly above-average memes. Just saying.)
Resume
More of my credentials.
Here are my work experiences and education.
Work Experience
Machine Learning Engineer Intern
Jan 2024 - Present
New York City, New York
Julius
At Julius, I helped untangle the chaos of inconsistent tags by building semantic similarity search and clustering pipelines . This brought structure, eliminated redundancy, and boosted data consistency—ultimately leading to cleaner training data and more context-aware LLM outputs. I also dove into statistical frequency analysis to surface dominant patterns and reduce noise in the tagging data, improving feature extraction for downstream ML models. It was a perfect blend of problem-solving, pattern-spotting, and my love for building things that make AI just... smarter.
AI Engineering Intern
Jun 2024 – Aug 2024
Milpitas, California
Virtusa
As part of the Content Localization and Assurance initiative at Virtusa, I designed and developed the initial prototype for an LLM-powered Advertisement Localization system that laid the groundwork for what would later become a key enterprise solution. My work focused on using LangChain, OpenAI APIs, and Prompt Engineering to automatically adapt ad content for diverse regional audiences 🌍. I also built a Recommendation Engine using Neo4j Knowledge Graphs and Hybrid Filtering to generate personalized suggestions, with results visualized using Plotly 📊. Additionally, I performed extensive data preprocessing and statistical analysis using NumPy and Pandas.
Machine Learning Intern
Jan 2023 – Jul 2023
Pune, India
Kanverse.AI
Engineered advanced similarity search algorithms using the Pinecone Vector Database, significantly improving retrieval accuracy and processing speed across large-scale accounting datasets. Analyzed and visualized data extracted from JSON files generated through PDF annotations for over 10,000 documents, leveraging Python, NumPy, Pandas, and Plotly to surface meaningful insights. Designed and implemented a YOLOv5-based Semantic Segmentation model to automate insurance claim processing by assessing vehicle damage from document images, enabling faster and more consistent decision-making.
Machine Learning Research Assistant
Nov 2021 – Jan 2023
Pune, India
Indian Council of Agricultural Research
As a Machine Learning Intern, I worked with the ML team on an AI + IoT-based agricultural monitoring system to support early detection of grape crop diseases. I developed and fine-tuned CNN and Mask R-CNN models using TensorFlow, enabling proactive disease identification that contributed to a 30–40% increase in crop yield. The project was part of a larger research initiative promoting sustainable agriculture through data-driven, AI-powered interventions.
Background Education
Master's Degree
August 2023 - May 2025
Binghamton University, State University Of New York
Getting my Master’s in Computer Science at Binghamton University, where I’ve been busy juggling Machine Learning, Operating Systems, Design Patterns, Data Mining, and Algorithm Design course classes — all while trying not to break my code (or my brain) 😅. It’s been a wild ride of debugging, deep dives, and occasional “aha!” moments at 2 AM.
Bachelor Of Technology
August 2019 - May 2023
MIT ADT University
Majored in Computer Science, specialized in Artificial Intelligence — where I spent four years building cool stuff, breaking it, obsessing over algorithm runtimes, debugging bugs that didn’t exist, and realizing that turning logic into something real is kind of magical. I also published a research paper with IEEE on ML and DL approaches for tuberculosis detection, marking my early dive into AI for healthcare.
Publications, Certifications & Projects
Check out my creations.
