Research to Real-World AI
Hi! I'm Devesh, an ML Engineer specializing in Federated Learning & Privacy-Preserving AI.
B.Tech in AI/ML (Honours) with 8.89 CGPA from University of Mumbai. 2 research papers under review at Springer. Currently building LLM-powered enterprise tools and privacy-preserving federated systems.
My Tech Stack
AI enthusiast with a passion for machine learning and intelligent systems.
Languages & Data
ML / AI
Security & Privacy
Tools & Frameworks
Projects
A selection of my recent projects and innovations in AI/ML.
Violence Detection System
Finalist at Rajasthan Police Hackathon. Real-time security system that detects weapons and violence in live camera feeds, sending alerts with location and timestamp to authorities.
BOTANIX
Finalist at Gujarat Hackathon. AI-powered plant identification system that scans plants and provides detailed information about their medicinal properties and traditional uses.
MedAI Assistant
Federated Learning system for early diagnosis of lung cancer and brain tumors from CT scans and MRI. Achieved 90%+ accuracy with secure architecture for medical deployment.
AI Image Generator
Web-based image generation tool using FLUX schnell LLM. Users can generate high-quality images from text prompts with an intuitive interface.
Smart OCR System
Invoice processing system using Qwen2vl-7b for visual document analysis. Extracts structured data into JSON format with deployed API for enterprise use.
Multi-Input Language Translator
Versatile translation tool supporting file uploads, text input, and voice input. Built with Llama model for accurate translations across multiple languages.
My Work Experience
ML Engineer
F.T. Solutions Pvt. Ltd.
June 2025 - June 2026→Built an LLM-based invoice extraction system to automate financial document processing
→Contributing to FT-DataMind, an internal AI platform providing zero-code access to live SAP data
→Working across the stack using React, LLM pipelines, DuckDB, and enterprise data sources
Intern
NET WORLD Technologies
Dec 2024 - March 2025→Completed 3-month intensive internship in software development
→Built web applications using modern frameworks and contributed to client projects
→Gained practical experience in project management and team collaboration
Technical Head
SORT & Literary Club
2022 - 2024→Led technical initiatives including full-stack website development for the club
→Delivered 2 professional digital magazines with cross-functional team leadership
→Improved online engagement and event participation through digital strategy
About Me
I'm an ML Engineer with a B.Tech (Honours) in AI & ML from University of Mumbai, specializing in Artificial Intelligence in Societal Benefits. My research focuses on federated learning and privacy-preserving systems — with 2 papers under review at Springer. Beyond coding, I enjoy reading books like “The Psychology of Money” and “The Shiva Trilogy” by Amish.
I believe in using AI to solve real compliance and privacy challenges — from GDPR-compliant medical imaging to zero-knowledge verified financial intelligence. My goal is to build intelligent solutions that automate manual work and eventually start my own company providing AI services in the finance sector.
Currently Working
Building innovative AI solutions and cutting-edge applications
ZK-FINet — Zero-Knowledge-Verified Federated Intelligence Network
Privacy-Preserving AIDesigned a federated learning framework integrating SCAFFOLD optimization with Zero-Knowledge Proofs to verify training correctness without exposing institutional data. Enabling privacy-preserving, regulator-auditable collaboration for financial intelligence tasks such as AML and fraud detection. System architecture aligned with real-world financial regulations and compliance constraints.
Technologies
Current Project
Implementing ZK-proof verification for distributed model training
Learning Resources
- ZK-SNARK protocols
- Financial compliance frameworks
- Federated learning security papers
FinGraph Sentinel — Graph-Based Financial Fraud Intelligence System
AI/ML SecurityAn AI-driven fraud detection system that identifies hidden relationships and suspicious transaction patterns using Graph Neural Networks (GNNs). Unlike traditional models that analyze transactions in isolation, FinGraph Sentinel models financial ecosystems as interconnected graphs to uncover fraud rings and coordinated illicit activity. Features GNN architectures (GCN, GraphSAGE, GAT) for node-level fraud classification, temporal behavior analysis, explainability using GNNExplainer, and interactive visualization of fraud clusters.
Technologies
Current Project
Building Graph, Intelligence, Behavior, and Explainability engines for relationship-driven fraud detection
Learning Resources
- GNN research papers
- Fraud detection datasets
- Graph database optimization
- GNNExplainer documentation
Cardano EdgePay — Offline-First AI-Optimized Microtransaction System
Blockchain + AIA decentralized microtransaction platform built on the Cardano blockchain, designed with an offline-first architecture to enable reliable payments in low-connectivity environments. Integrates lightweight AI models to optimize transaction routing and minimize fees. Features local queuing with delayed blockchain synchronization, Plutus smart contracts, AI-based fee optimization, and intelligent transaction routing for efficient settlement.
Technologies
Current Project
Building offline layer, sync engine, and AI-powered intelligence layer for optimized transactions
Learning Resources
- Cardano developer docs
- Plutus smart contracts
- Offline-first design patterns
- AI fee optimization research
Global Collaboration
Building relationships across time zones and connecting with teams worldwide
Flexible Time Zones
Available across multiple time zones
Prioritize Collaboration
Team-first approach to development
Available Worldwide
Global reach and accessibility
Quick Responses
Fast communication and delivery