Summary
Passionate Machine Learning and MLOps enthusiast with a strong foundation in Computer Science. Experienced in developing ML models, implementing MLOps practices, and creating web applications.
Skills
Languages:
C/C++, C#, Python, JavaScript, TypeScript, MATLAB, HTML, CSS
Libraries:
Pandas, Numpy, Scikit-Learn, OpenCV, PyTorch, TensorFlow, Matplotlib, Seaborn, NLTK
Frameworks/Tools:
Docker, React.js, Node.js, MongoDB, Git, Unity, Azure, mlflow, ZenML, Flask, Dagshub, Firebase, Django, Flutter, Linux, Transformers, LLMs
Education
PES University
Nov 2022 - PresentBangalore, India
B.Tech in Computer Science and Engineering
Experience
IEEE CS, PES University
ML Research Intern
Bangalore
- Architected and implemented a novel hybrid GAN-VAE system for artistic style transfer, combining an encoder for feature extraction with advanced generative networks, achieving 40% improved style preservation compared to baseline models
- Led comprehensive comparative analysis of multiple GAN architectures (Cycle GAN, Attention GAN), optimizing the final three-component architecture through systematic experimentation and achieving state-of-the-art results in artistic transformation quality
Toyota Kirloskar Motor
Data Science Intern
Bangalore, India
- Developed and deployed an enterprise-level automated sentiment analysis system using NLTK and Python, revolutionizing review processing from 7 hours to 10 seconds, while enabling seamless access for 100+ employees across company LAN infrastructure
- Engineered a real-time data analysis pipeline with 99% efficiency improvement (4 hours to 5 seconds), incorporating robust DevOps practices for deployment and maintaining 100% data accuracy across multiple analytics solutions
CDSAML, PES University
Research Intern
Bangalore
- Developed a breakthrough Kannada-to-English speech translation pipeline surpassing Google’s Long Model benchmarks, implementing a comprehensive seven-stage system for real-time processing
- Engineered state-of-the-art audio processing modules incorporating advanced ML techniques (denoising, enhancement, segmentation, transcription), achieving superior accuracy in cross-lingual translation while maintaining real-time performance
Projects
DevForge
- Engineered an AI-powered development environment integrating LLMs with containerized runtimes, automating code generation, execution, and error-correction to enhance developer productivity and code reliability
- Developed a web-based IDE with seamless project management and interactive console access, orchestrating communication between Linux filesystem, cloud containers, and AI models for a streamlined end-to-end development workflow
MLOps - Credit Card Fraud Detection
- Architected and implemented a comprehensive end-to-end MLOps pipeline for credit card fraud detection, encompassing robust data ingestion, preprocessing, model training, and production deployment workflows
- • Established a scalable MLOps infrastructure with version control, CI/CD, automated testing, and advanced experiment tracking/model management using DagsHub and MLflow, ensuring reproducibility and maintainability of machine learning systems
Cropify
- Developed a full-stack agricultural assistance platform integrating React.js frontend and Flask backend, leveraging ML/DL models to provide personalized crop and fertilizer recommendations based on environmental and location-based data
- Implemented real-time plant disease detection using deep learning, with automated diagnosis and treatment recommendations, demonstrating the practical application of computer vision techniques to empower data-driven farming
Activities & Achievements
Practical Approach to ML
Won 1st place in the hackathon organised by CIE PESU (Apr 2024)
Appex | Technical Lead
Organized 24-hr hackathons and ideathons for 200+ students and provided mentorship and resources.
Aura | Technical member
Organized "Crayion", a hackathon focused on image generation using advanced models such as DALL-E and MidJourney and served as mentor for the event.