AI/ML Engineer & Data Science Graduate Student
AI/ML Engineer with 2 years of experience developing and deploying LLM and RAG-based applications for enterprise and research use cases. Currently pursuing MS in Engineering Data Science and AI at University of Houston. Focused on translating complex AI concepts into scalable, production-ready solutions with real-world impact.
I'm a passionate AI/ML engineer and researcher currently pursuing my Master's in Engineering Data Science and AI at the University of Houston. My work focuses on developing intelligent systems powered by Large Language Models, Retrieval-Augmented Generation, and Deep Learning.
With professional experience at Accenture and research background at Samsung PRISM, I specialize in building production-ready AI solutions, explainable AI frameworks, and scalable ML pipelines. My research interests span Machine Learning, Generative AI, Explainable AI (XAI), Computer Vision, and Reinforcement Learning.
Deep learning, LLMs, RAG systems, explainable AI (XAI), computer vision, and reinforcement learning
Full-stack development with focus on AI application deployment and REST APIs
Azure cloud services, ML pipeline automation, and model deployment expertise
Enterprise LLM-based reverse engineering platform with function calling and advanced prompt engineering, reducing manual code migration effort by 70% for cross-language transformations.
Developed a Deep ensemble learning model combining VGG16, MobileNet, InceptionV3, and Xception achieving 97.6% accuracy on corn leaf disease classification using data balancing and image augmentation.
Built a Deep Q-Learning agent from scratch in PyTorch to autonomously play the 2048 game, achieving highest tile of 1024 through reward shaping and iterative training.
Performed a comparative analysis of transformer-based and graph-based embeddings, evaluating semantic fidelity using ROUGE metrics to optimize text summarization performance.
ICCCNT 2023 • June 2023
Developed a deep-learning model that converts voice commands into spectrograms for real-time navigation. The system interprets spoken instructions to enable hands-free maze traversal through audio input processing and deep neural networks.
ICTIS 2023 • April 2023
Created a computer-vision gesture interface enabling virtual mouse and keyboard control with real-time hand-tracking. This research explores OpenCV techniques to build an intuitive human-computer interaction system controlled entirely through webcam-captured hand gestures.
Accenture • Nov 2023 – Aug 2025 • Hyderabad, India
Built scalable AI features for the GenLite reverse engineering platform using LLMs, function calling, and advanced prompt engineering to enable seamless cross-language code transformation. Designed and implemented a code conversion module with integrated preprocessing pipelines, reducing manual code migration effort by over 70%. Delivered multiple POC projects for RAG applications and AI chatbots, with several promoted to full-scale development and production deployment.
Samsung PRISM • Sep 2021 – Apr 2022
Researched model interpretability with a team of 6 using SHAP, CEM, and LIME, achieving a 40% improvement in explainability. Developed an explainable AI (XAI) framework for production ML systems to reduce reliance on black-box models and improve interpretability.
University of Houston • Aug 2025 - May 2027
CGPA: 4.0/4.0
Amrita School of Engineering, Bengaluru • June 2019 - May 2023
CGPA: 8.43/10
I'm actively seeking new opportunities and collaborations where I can contribute to innovative, data-driven products.
My inbox is always open — whether you have a question or just want to say hi, I'm happy to chat about AI, tech, or anything in between!