Rajarajeswari Palacharla
Tech Enthusiast
Tech Enthusiast
I’m a passionate and curious Software Engineer with a Master’s degree in Computer Science from Texas A&M University, driven by a deep fascination for AI, machine learning, and data analytics. My journey started with a solid foundation in Electronics and Communication Engineering from NIT Hamirpur, giving me a unique edge across hardware and software domains.
At Samsung R&D, I took on the challenge of developing and optimizing over 280 APIs in just two months using asynchronous programming, dramatically enhancing the SmartThings IoT platform’s speed and reliability. Beyond coding, I’ve built intelligent systems powered by machine learning and large language models, always pushing the boundaries of what technology can achieve.
What excites me most is the constant opportunity to learn and grow. I thrive in environments where I can explore new ideas, tackle complex problems, and create solutions that truly make an impact. With a diverse skill set and an insatiable appetite for knowledge, I’m eager to contribute to innovative projects that shape the future.
Let’s connect and explore how we can collaborate to drive technological advancements together!
• Languages: C/C++, Python, C#, MATLAB, Ruby, JavaScript, SQL, WebGL
• Front-end: Vue.js, React.js, HTML5/CSS3, JavaScript, D3.js, Chart.js, Bootstrap, Figma, Wix, MVC/MVVM
• Back-end: Flask, Django, Ruby on Rails, .NET Framework, REST & gRPC APIs, Search Engines
• Machine Learning: Algorithms, NLP, Information Retrieval, Generative AI, Data Visualization, Deep Learning, LLM Fine-tuning, RAGs, Recommender Systems, Computer Vision, Data Analysis, Pandas, NumPy, Tensorflow, Keras, Pytorch, Scikit-learn, Matplotlib, NLTK
• Cloud & DevOps: AWS, Docker, Kubernetes, Terraform, Jenkins, CI/CD, GitHub Actions
• Tools: Git & GitHub, JIRA & Confluence, VS Code & Visual Studio, Tableau, ShaderToy, Figma, Linux, MS Office Suite
• Other: OOP, Data Structures & Algorithms, SDLC, Agile Methodologies, Unit Testing & TDD, Project Management
Master of Science, GPA: 4.0
Course Work: Analysis of Algorithms, Software Engineering, Operating Systems, Parallel Computing, Machine Learning, Deep Learning, Computational Photography, Artificial Intelligence, Information Storage & retrieval, Data Visualization, Cyber Security Risk, Image Synthesis, Project Management
Bachelor of Technology, CGPA: 9.28/10
Relevant courses: Data Structures, Computer Networks, Microcontrollers/Processors, Signal Processing, Engineering Mathematics
Driving the shift from manual to AI-powered analytics by analyzing clinical databases using Python and statistical visualization. Evaluating LLM/RAG pipelines to automate therapeutic insight generation and enhancing user experience through React-based history tracking with integrated data visualization.
Developed a clinician-clone assistant using multi-agent LLM workflows for summarization, NER, and treatment recommendations with a fine-tuned Mistral model. Optimized prompt strategies and workflow efficiency, saving 18 minutes per specialist per case through automated summarization and recommendations.
Led programming lab sessions, guiding students through GPU programming (WebGL), and image processing concepts. Provided one-on-one mentorship, helping students understand complex topics like parallel computing and shader programming. Assisted in curriculum development, graded assessments, and ensured clarity in evaluation.
Designed and optimized APIs for the SmartThings platform using C#, enhancing smart device automation and user experience. Spearheaded the development of 500+ test cases, reducing API response time by 40% and ensuring robust system performance. Collaborated with cross-functional teams to deploy high-quality software solutions, balancing rapid iteration with rigorous testing.
Developed a real-time fraud detection system leveraging machine learning, cross-referencing facial features with database records to prevent identity fraud in examination halls. Integrated OCR technology, automating data capture and cutting manual workload by 50%, while boosting efficiency by 30%.
CinePrompt is an intelligent movie recommendation engine that delivers personalized results in real-time by combining prompt-based search with user preferences. Built using FAISS, Flask, and collaborative filtering, the system supports prompt-based querying across a 1M+ movie catalog. A re-ranking module combines SVD++ scoring with LLaMA 2-based rationale generation, achieving 12% higher Precision@5 compared to traditional systems.
VitaFin is a dual-panel platform that helps users monitor both health and financial goals. Featuring real-time JSON streaming, Chart.js visualizations, and Vue.js interactivity, the dashboard supports A/B-tested layouts, flagging unusual activity through outlier detection algorithms. The system led to a 22% increase in user satisfaction during pilot testing.
This Rails-based web app streamlines the process of tracking continuing education credits for academic professionals. With PostgreSQL optimization and GitHub Actions CI/CD, it offers secure login, real-time alerts, and admin tools. The project earned 100% client approval and maintains 98% test coverage via RSpec and Cucumber.
