AI & Machine Learning
Engineering Student
Building practical, AI-powered applications. I combine rapid backend prototyping with clean, intuitive frontend design to make everyday tasks easier.
About Me
My professional objective and focus.
I am an Artificial Intelligence and Machine Learning Engineering student who enjoys turning ideas into practical, user-focused products. My interest in technology began in school through simple web development experiments and grew into a passion for building solutions that make everyday tasks easier.
Over time, I became particularly interested in AI and Machine Learning because of their ability to uncover patterns, automate processes, and create intelligent applications that solve real-world problems. Since then, I have worked on projects that combine AI, software development, and modern web technologies to transform ideas into functional products.
My approach to development is highly iterative. I enjoy rapidly prototyping, testing concepts, and refining solutions based on user needs and feedback. While I leverage modern AI tools to accelerate development, I focus on product design, problem-solving, user experience, and ensuring that the final solution delivers real value.
I am currently seeking opportunities to learn from experienced engineers, deepen my understanding of scalable software systems, and contribute to teams building impactful technology. My goal is to combine strong engineering fundamentals with AI-driven innovation to create products that are both useful and meaningful.
Technical Skills
Technologies and tools I work with.
Languages
Frontend Frameworks
Backend Frameworks
Databases & Cloud
AI & ML Tools
DevOps & Automation
Works seamlessly with your tech stack
Integrating with my favorite technologies
Featured Projects
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AutoVue is a real-time vehicle telemetry dashboard that visualizes live OBD-II sensor data, connects to a Python ML backend through an Express proxy, and provides predictive maintenance insights along with driver behavior monitoring.
- Built a real-time dashboard that streams live vehicle telemetry using REST APIs and WebSockets.
- Designed a decoupled architecture with React frontend, Express proxy, and Python ML backend for scalable deployment.
- Supports dataset playback controls, live sensor visualization, vehicle health monitoring, and driver behavior analysis.
AI system that turns scanned question papers into structured, searchable data — extracting content with Gemini Vision, classifying questions by Bloom's Taxonomy, and generating balanced exam papers in under 60 seconds.
- 🥉 3rd Place — CLPBL Project Showcase, St. Joseph Engineering College.
- Built module-wise heatmaps and Bloom's Taxonomy analytics dashboards.
- AI paper generator respects marks distribution and module coverage.
Real-time fraud detection system that classifies debit card transactions as fraudulent or legitimate using a Snap Random Forest Classifier hosted on IBM Cloud, with a live analytics dashboard and CSV batch detection.
- End-to-end pipeline from transaction input to IBM Watson ML prediction.
- Live dashboard with fraud/legitimate ratio, alerts panel, and history logs.
- Fully connected to a live Node.js backend — no mock data.
Mobile-first event website for a community 5K run, village clean-up drive, and treasure hunt — serving as the single source of truth for event details, rules, and Google Form registration.
- First project used by real people for a real event, not just a demo.
- Designed for a non-technical, village-based audience.
- Deployed on Cloudflare Pages for fast, free global delivery.
Education & Achievements
Academic background and key progress.
B.E. in Artificial Intelligence & Machine Learning
CGPA: 9.34 / 10.0
Relevant Coursework: Machine Learning, Database Management Systems (DBMS), Operating Systems (OS), Deep Learning.
Class 12 (Pre-University)
Score: 92.5%
Completed core science curriculum with high distinction.
Class 10 (High School)
Score: 95.68%