I’m a developer and learner passionate about building at the intersection of Artificial Intelligence, Machine Learning, and Cybersecurity — crafting systems that are not just smart, but also secure and resilient.

💡 I believe the future of digital systems depends on intelligent security — where ML isn’t just about prediction, but about protection, detection, and adaptation.

🎯 My focus areas include:

• AI-driven Threat Detection (malware analysis, anomaly detection, behavioral learning)
• Adversarial Machine Learning & Explainable AI
• Secure ML Pipelines & Data Privacy
• Cross-domain innovation — blending cybersecurity insights with full-stack and mobile development.

• Machine Learning for Cyber Defense — using ML to detect anomalies, intrusions, and fraud.
• Synthetic Data Generation — building realistic datasets for model training using LLMs.
• Offensive & Defensive Security — pentesting, vulnerability research, and automation.
• Secure Backend Systems — integrating AI with decentralized and cloud-native architectures.
• Smart Flutter Apps — embedding AI models and privacy-first design into mobile apps.

AI/ML Intern – Infosys SpringBoard
September 2025 – Present

• Developing a AI-Based Music Composition System: "Whispers of the Wires"

Flutter Developer Intern – ClubChat (IIIT Delhi)
July 2025 – September 2025

• Helped launch core features for a mobile app that now connects over 10,000 college students across India, seeing the direct impact of my work on real users.
• Tackled more than 20 bugs and took on UI/UX improvements, making the app more intuitive and enjoyable for the student community.
• Was part of two major feature rollouts, collaborating with designer, backend engineers, to turn ideas into live experiences during fast-moving agile sprints.
• Wrote modular, scalable code, contributed to documentation, and actively took part in peer reviews to keep our codebase clean and maintainable.
• Enjoyed the autonomy of remote work, reporting directly to the Tech Head, participating in daily planning, and iterating quickly thanks to daily feedback.
• Learned firsthand how a startup team collaborates and problem-solves, deepening my appreciation for the process behind building a product from scratch.

Open Source Contributor – Synthetic Data Generator
2024 – Present

• Created a domain-agnostic synthetic dataset generator.
• Integrated Hugging Face models for AI-driven field and data synthesis.
• Working on ML model and training the same.

A domain-agnostic AI tool that generates realistic, contextual datasets using Hugging Face models.
Highlights: Field auto-generation, contextual dataset synthesis, CSV/Excel export, web UI.
Tech Stack: Flask • Streamlit • Transformers • Pandas • Python
Use Case: Helps ML developers train models when real datasets are unavailable.
🔗 Repository Link

An AI-powered financial management and personalized money mentor app that helps students and professionals manage budgets intelligently.
Highlights: Personalized insights, anomaly detection, budget forecasting, and ML-powered advice.
Tech Stack: Flutter • Supabase • Dart • Cloud Firestore • ML Kit
Inspiration: Built during a hackathon under the theme “Let AI speak to your money,” "Mumbai Hacks," "SIH 2025- Student Innovation- EdTech/FinTech" 🔗 Repository Link

A Flutter-based wellness application developed as part of the SAP Hackfest, designed to interface with a wearable smart insole for women. The app monitors foot pressure data and dynamically adapts massage responses for enhanced comfort and health support.
Highlights: Real-time sensor data simulation, multi-user profile management, adaptive massage control, SAP HANA integration for analytics, and AI-driven personalization.
Tech Stack: Flutter • Dart • Provider • SAP HANA (Trial) • Node.js/Flask (Planned) • fl_chart
Inspiration: Built for the SAP Hackfest 2025, focusing on IoT-enabled comfort, women’s wellness, and adaptive AI experiences.
🔗 Repository Link

A custom mini operating system shell with an integrated offline LLM assistant capable of local interaction and command understanding.
Highlights: Lightweight shell built in C, local AI inference without internet access.
Tech Stack: C • OS-level programming • Offline LLM • System-level AI Integration
Vision: Privacy-first AI interaction for secure offline environments.
🔗 Repository Link

A Flutter-based job-finding app connecting local employers and job seekers.
Highlights: Real-time job listings, location filters, and secure authentication.
Tech Stack: Flutter • Firebase • Google Cloud
Goal: Democratize access to local employment opportunities.
🔗 Repository Link

Blockchain + ML-powered system that ensures fair and secure organ donor–recipient matching.
Highlights: Synthetic data generation, ML compatibility scoring, transparent blockchain logging.
Tech Stack: Python • Machine Learning • Blockchain • Flask
Goal: Enhance healthcare trust with AI-driven transparency and traceability.

Hackathon project to detect untracked or suspicious data flows in decentralized systems.
Highlights: ML-based anomaly detection, data isolation for security analysis.
Tech Stack: Python • ML • Network Analysis • Blockchain Concepts
Impact: Helps security teams identify and mitigate hidden network threats.

• Hostel Permission Management System – Full-stack (Firebase + React) app that simplifies hostel gate pass management for students and wardens.
• Schedule Planner / Manager – A modular app for managing class schedules and personal productivity.

• Air Notepad – A gesture-controlled Flutter app that lets users draw or write on a virtual whiteboard using computer vision.
• Decentralized Cloud Storage (IPFS) – A privacy-focused file storage app with encryption and distributed architecture.

Languages:

Frameworks & Tools:

Databases & Cloud:

AI/ML & Data:

Cybersecurity:

• 🔹 Adversarial ML & Explainable AI
• 🔹 AI-driven Cyber Defense
• 🔹 Privacy-Preserving Machine Learning
• 🔹 Secure Federated Learning
• 🔹 Deploying AI on Edge & Offline Systems

“The next frontier of cybersecurity isn’t firewalls — it’s intelligence.”
— Vaibhav Sharma

I often test AI models like I test systems — by trying to break them intelligently.
When I’m not coding, I’m either exploring new exploits or debugging life with caffeine ☕.

⭐ “Building the bridge between Intelligence and Security.” ⭐