Full Stack
Developer
Passionate about Generative AI, Full Stack Development, and Entrepreneurship. I help businesses build innovative solutions that make a real-world impact.
About me.
Developing innovative solutions with cutting-edge technology is what I love doing, and that's why I give my all in every new challenge. From AI research to full-stack development, I'm passionate about creating impactful digital experiences.
Education
Bachelor of Engineering in Computer Science with Honors in Data Science
Pune University (2020–2024)
Experience
Currently Specialist Programmer at Infosys
2+ years of professional experience
Expertise
Full Stack Development & AI Research
Focus on innovative solutions
Entrepreneurship
Founded GenMedia - AI-powered media generator
Published research & sponsored by BrainCells
My Tech Stack
Experience.
My professional journey spans across various roles in software development, AI research, and entrepreneurship.
Specialist Programmer
Infosys
Engineered and maintained scalable microservices using Java Spring Boot for Infosys Helix, an AI-first healthcare platform focused on automating and optimizing care delivery through intelligent analytics
Developed a robust file processing module to seamlessly handle large volumes of vendor and customer data, significantly reducing processing time while ensuring compliance with healthcare regulations (e.g., HIPAA)
Spearheaded the database migration from legacy systems to modern architectures using MongoDB and PostgreSQL, resulting in a 40% improvement in data retrieval performance and overall system responsiveness
Leveraged Kubernetes for efficient deployment, scaling, and orchestration of microservices across distributed environments
Founder
GenMedia
Founded and led GenMedia, a Generative AI-powered platform enabling personalized media content creation by integrating Stable Diffusion for image generation and AudioCraft for AI-generated audio
Launched the MVP focused on generating customized birthday and achievement wishes by leveraging user-specific datasets, such as personal images and music preferences
Secured strategic sponsorship from BrainCells, facilitating the platform's expansion into AI-driven marketing media for businesses to create tailored advertisements and promotional assets
Scaled the platform to support automated generation of product-focused content, including AI-crafted promotional images and short videos for consumer goods (e.g., customized coffee mugs and lifestyle items)
Embedded C/C++ Intern
KPIT
Developed and integrated device drivers for embedded systems to enable efficient hardware–software interaction across automotive and industrial applications
Implemented communication protocols such as MODBUS and UART to facilitate reliable data exchange between microcontrollers and external peripherals
Engineered and optimized firmware for the MSPM0G3507 microcontroller, achieving improved system responsiveness and reduced latency
Conducted real-time debugging, diagnostics, and performance tuning, significantly enhancing the stability and reliability of embedded platforms
Research Intern
7DNA Analytica
Investigated the impact of nicotine addiction on brain function, focusing on early brain disease detection using fMRI/MRI neuroimaging data
Preprocessed and post-processed complex 4D neuroimaging datasets with tools like Nibabel and Nilearn, enhancing image clarity and enabling high-quality analytical insights
Designed and implemented graph-based analysis algorithms to uncover functional connectivity patterns and anomalies in brain activity, contributing to more accurate disease diagnostics
Advanced healthcare research by improving the efficiency and precision of brain disease detection through cutting-edge neuroimaging and computational techniques
Summer Intern
Persistent Systems
Trained in Computer Science fundamentals, including OOP, DBMS, Computer Networks, and Web Development, as part of Persistent's Martian Program
Developed a Library Management System using the MEAN stack (MongoDB, Express.js, ReactJS, Node.js), implementing a full-stack solution for managing library operations
Designed and integrated RESTful APIs to enable seamless interaction between the frontend (ReactJS) and backend (Node.js + Express.js) with MongoDB as the database
Web Developer & Business Developer Intern
RSB Infotech
Designed and developed the company's official website rsbinfotech.com using the MERN stack (MongoDB, Express.js, React.js, Node.js), collaborating with a team of four to build a scalable and responsive platform
Contributed to business development efforts by crafting and executing marketing strategies, designing ads and promotional media, and aligning content with brand goals
Led SEO optimization efforts, improving the website's visibility on search engines and driving organic traffic growth
Cybersecurity Intern
Palo Alto Networks
Worked on honeypot deployment and monitoring to simulate real-world vulnerable systems and analyze attack patterns in a controlled environment
Simulated offensive cybersecurity attacks, including Man-in-the-Middle (MITM), Social Engineering, DDoS, and SQL Injection using Kali Linux and other penetration testing tools
Gained hands-on experience with ethical hacking techniques, network traffic analysis, and threat detection strategies to identify and mitigate vulnerabilities
Featured Projects.
Showcasing innovative projects that demonstrate expertise in AI research, machine learning, and full-stack development.
GenMedia
AI-Powered Personalized Media Generator
Revolutionary platform that generates personalized images, music, and videos using advanced AI models. Sponsored by BrainCells and published research paper on comparative study of different image and audio generation AI models
Key Achievements
Optimized Stable Diffusion pipeline for 40% faster generation
Integrated AudioCraft for high-quality music synthesis
Developed custom personalization algorithms
Published research paper on AI-driven media personalization
Secured sponsorship from BrainCells for innovation
Technologies Used
Animal Image Classifier
CNN-Based Classification System
High-performance computer vision system for animal species classification using Convolutional Neural Networks, achieving exceptional accuracy through advanced deep learning techniques.
Key Achievements
Achieved 97% classification accuracy
Implemented custom CNN architecture
Optimized for real-time inference
Deployed scalable inference pipeline
Comprehensive evaluation on diverse datasets
Technologies Used
Blogger
Full-Stack Blog Application
Full-stack blog application using HTML, CSS, EJS, NodeJS, ExpressJS, and MongoDB. Features session-based authentication and data models with reference storage method.
Key Achievements
Session-based authentication system
RESTful API design with Express.js
MongoDB data modeling with references
Server-side rendering with EJS
Responsive frontend design
Technologies Used
Vault-E
Decentralized Storage System
A decentralized storage system using blockchain technology. Ganache and Truffle are used for private blockchain networks on Ethereum and Solidity for smart contracts.
Key Achievements
Blockchain-based decentralized storage
Smart contract development with Solidity
Private Ethereum network setup
Truffle framework implementation
Secure data storage and retrieval
Technologies Used
Research Publications.
Contributing to the advancement of AI research through published papers and innovative approaches to multi-modal media generation.
GenMedia: A Research Project on Personalized Multi-Modal Media Generation Using Stable Diffusion and AudioCraft
Abstract
In this paper, we present GenMedia, a research project focused on advancing the capabilities of Stable Diffusion models and AudioCraft's MusicGen for personalized multi-modal media generation. The project explores the fine-tuning of Stable Diffusion 2.0 and 3.5 using DreamBooth, with a particular emphasis on advancements in latent space optimization, prompt adherence, and image quality. These improvements enable the generation of high-quality, context-aware images based on user-provided text prompts and personalized datasets. Additionally, we investigate the fine-tuning of AudioCraft's MusicGen using Dora to synthesize personalized audio content, leveraging custom instrumental datasets and text prompts. The integration of FFMPEG enables the seamless combination of generated images and audio into cohesive video outputs. Through extensive experiments, we evaluate the performance of these models, focusing on their ability to create highly personalized and contextually relevant media content. This research highlights the potential of advanced generative models to revolutionize multi-modal AI, paving the way for future innovations in personalized media generation.
Research Topics
AI Enhanced Sonification of 3D Datasets for Improved Accessibility and Insight Using Python Based Data Processing and Visualization Libraries
Abstract
Data sonification, the translation of data into sound, offers a complementary modality to visual representation, especially for complex multidimensional datasets. This paper introduces Sonific, an AI enhanced pipeline designed to sonify three dimensional datasets, such as point clouds, by combining machine learning–based feature extraction with spatial audio rendering. Unlike prior methods relying on raw data or bespoke mappings, Sonific automatically identifies salient structures clusters, surfaces, and anomalies and maps them to distinct auditory cues in a 3D soundscape. Our prototype demonstrates improved perceptual differentiation of shapes and spatial features compared to raw sonification approaches. The system fosters accessibility for visually impaired users, supports scientific data analysis, and opens new pathways for interactive multimodal data exploration. We discuss implementation details, prototype evaluation, and potential applications in scientific research, education, assistive technology, and creative domains.
Research Topics
AI-Driven Evolutionary Honeypots for Polymorphic Cyber Threats
Abstract
Polymorphic cyber threats continuously modify their code and behavioral patterns to circumvent traditional detection mechanisms, creating substantial challenges for conventional security frameworks. Honeypots, which function as decoy systems designed to attract attackers while logging their methodologies, provide a valuable defensive approach by capturing detailed attacker behaviors. This research introduces a proof-of-concept AI-driven evolutionary honeypot framework that combines transformer-based attack sequence prediction with reinforcement learning adaptation to combat polymorphic malware attacks. The evaluation utilized the Kaggle Polymorphic Malware Dataset 2025 across multiple threat categories. The transformer-based model achieved competitive performance with 81.68% accuracy, approaching traditional ensemble methods such as Random Forest (82.06%) while substantially outperforming deep learning baselines including BiLSTM (72.14%). The reinforcement learning adaptation component demonstrated practical feasibility with an 8% meaningful adaptation rate across 100 attack sequences, with Email Server configurations achieving 34.263 average engagement compared to 6.229 overall. Statistical significance testing confirmed large effect sizes compared to deep learning approaches (Cohen's D = 3.579 vs BiLSTM) while revealing that ensemble methods maintain slight advantages for this data type. The framework establishes the first integrated transformer + RL system for adaptive honeypot deployment, providing a foundation for future research in evolutionary cybersecurity defense. The research contributions include rigorous experimental methodology, comprehensive baseline comparisons, transparent performance assessment, and a complete Python implementation suitable for continued development.
Research Topics
Get In Touch.
Ready to start your next project or new venture? Let's discuss how I can help bring your ideas to life with innovative technology solutions.
Let's Connect
I'm always excited to work on new projects and collaborate with like-minded individuals. Whether you need full-stack, AI/ML, Data Science engineer or a co-founder for starting a new venture, I'm here to help.
chinmaykamble@gmail.com
Phone
+91 7722036322
linkedin.com/in/chinmaykamble
GitHub
github.com/Chinmayk2002
Available for remote work and collaboration worldwide