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Hello, I'm Adith
Software Engineer | Cloud & Machine Learning Specialist
I am a seasoned software engineer focusing on cloud architecture and machine learning, currently interning as a Cloud Engineer at Rheem Manufacturing. I’ve developed data-driven APIs using FastAPI and Flask, integrated Elasticsearch for rapid retrieval, and cut operational overhead by 30%. Previously at Unique World Robotics, I built real-time crop prediction APIs with 97% accuracy and migrated infrastructure to AWS, reducing costs by 30%. While pursuing an M.Sc. in Computer Science at NYU (3.97/4.0 GPA), I also conduct AI research at AI4CE Lab, leveraging TensorFlow and PyTorch to tackle complex challenges. By blending software engineering and AI, I aim to deliver transformative solutions that drive growth and efficiency.
Education
New York University
Masters in Computer Science
Currently pursuing an M.Sc. in Computer Science at New York University with a 3.97/4.0 GPA. My coursework spans Machine Learning, Deep Learning, Computer Vision, Operating Systems, and Java Programming, complemented by a Marketing elective at NYU Stern—ensuring a solid mix of advanced technical and business acumen. Alongside my studies, I am interning as a Cloud Engineer, gaining hands-on experience in deploying APIs, optimizing cloud infrastructure, and building scalable, data-driven solutions.
National Institute Of Technology,Warangal
Bachelor Of Technology-Computer Science
Studied at the National Institute of Technology Warangal—one of India’s highly competitive engineering institutions—where I completed rigorous coursework in Cloud Computing, Data Science, Data Mining, Distributed Systems, and Database Management Systems (DBMS). My bachelor’s thesis centered on developing a review-based group recommender system using deep learning techniques, aiming to enhance user recommendations through advanced AI methodologies.
Professional Highlights
Software Engineer (AI and Cloud)
Rheem Manufacturing May 2024-Present
At Rheem Manufacturing, I developed Python-based RESTful APIs using FastAPI—serving over 10,000 daily requests—and implemented a self-managed Elasticsearch stack, boosting data retrieval speed by 40%. My contributions were integral to driving projects into production, including real-time analytics pipelines that integrated AWS S3 for large-scale data ingestion and enhanced insights for connected devices.
Software Engineer Intern
Alpha Data Dubai,UAE May 2022-Aug 2022
At Alpha Data, I automated manual workflows by developing RPA bots, reducing operational effort by 80%. Using Python and UIPath, I streamlined repetitive tasks, enhancing productivity by 30%.
Software Engineer
Unique World Robotics May 2021-Aug 2023
Built an ML algorithm for crop pattern prediction with 97% accuracy, leveraging PyTorch and TensorFlow with DHT11 sensor data. Deployed the solution on AWS EC2 and S3, reducing infrastructure costs by 30%.* Created RESTful APIs to streamline IoT data flow to cloud storage, increasing solution adaptability.
Academic Experience
Research Assistant
Sep 2024- Present
I am contributing to the OpenFAST software development on the computer science side, focusing on enhancing simulation capabilities for offshore wind energy systems. My role involves optimizing and integrating computational models to improve the performance of wind turbines in dynamic ocean environments. By working with complex codebases and simulations, I’m helping drive innovations in renewable energy through advanced software solutions.
Course Assistant(ML)
Sep 2024-Present
As a course assistant for both graduate (CS-GY 6923) and undergraduate (CS-UY 4563) Machine Learning courses, I assist in preparing course materials, grading assignments, and providing support to students during office hours. I collaborate with professors to ensure students grasp key concepts in machine learning, deep learning, and algorithmic problem-solving. Additionally, I help facilitate discussions, explain complex topics, and guide students through hands-on coding exercises and projects.
Undergraduate Research Assistant
Aug 2022-Aug 2023
As a research assistant, I developed a review-based group recommender system using deep learning techniques, including CNN and attention mechanisms. My research aimed to improve recommendation accuracy by leveraging user reviews, focusing on group decision-making dynamics. This work involved extensive experimentation with neural networks, data preprocessing, and model optimization, contributing to advancements in personalized recommendation systems.
If you're interested in collaborating on cutting-edge technology, or if you just want to say hello, feel free to reach out!