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AI Engineer with 1+ years in Flutter Development, Machine Learning & Distributed Systems
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Software engineer with 1.5 years of Flutter development experience and a growing focus on machine learning and distributed systems. Built and deployed mobile applications across Android and iOS, with hands-on experience in NLP, computer vision, and predictive analytics.
Hochschule Aalen
Master of Science · Machine Learning and Data Analytics
August 1, 2025 – Present
Gujarat Technological University
Bachelor of Engineering · Computer science and Engineering
August 1, 2020 – June 30, 2024
Weapplinse Technology
AI/ML Developer Intern
April 1, 2025 – July 1, 2025
Surat, Gujarat, India
Weapplinse Technology
Flutter Developer
January 1, 2024 – March 1, 2025
Surat, Gujarat, India
Hochschule Research Paper: NewSQL and Distributed SQL: A Literature Review
January 1, 2025 – June 1, 2026
Conducted a systematic literature review (Kitchenham methodology) across IEEE Xplore, ACM, Google Scholar, and SpringerLink Analyzed and compared architectural characteristics of NewSQL (VoltDB, NuoDB) and Distributed SQL systems (Google Spanner, CockroachDB) Examined consensus protocols (Paxos, Raft), ACID/BASE consistency models, and the CAP theorem in the context of geo-distributed databases Identified 7 open research gaps including geo-distributed transaction latency, HTAP workload support, and elastic scaling
Smart Expense Tracker with AI Insights
January 1, 2024 – June 1, 2026
Developed a cross-platform expense tracking app using Flutter & Dart, supporting both Android and iOS from a single codebase Integrated Gemini AI API to generate personalized spending insights based on user transaction history Used Hive for offline-first local storage and Firebase Firestore for real-time cloud sync across devices Implemented biometric authentication (Face ID and Fingerprint) using the local_auth package for secure access
fifty50 flip.today
January 1, 2024 – March 1, 2025
Contributed to development of fifty50 flip.today, a cross-platform social gaming app, implementing UI flows including sign-up, menu, gaming, and support screens using Flutter and Dart Delivered weekly app updates and debugging support, maintaining application stability across Android and iOS Collaborated with UI/UX designers to ensure pixel-perfect implementation across different screen sizes and devices Worked on multiple client mobile applications across industries including transport, maintenance, and enterprise tools
Explainable Anomaly Detection – Household Power Consumption
January 1, 2024 – June 1, 2026
Built an end-to-end anomaly detection system on the UCI Individual Household Electric Power Consumption dataset (2M minute-resolution records) Implemented and compared Isolation Forest and LSTM Autoencoder models for unsupervised anomaly detection across 7 electrical features Applied model-agnostic explainability techniques including SHAP, LIME, and DiCE counterfactuals to interpret and visualize detected anomalies Designed a modular pipeline covering data ingestion, feature engineering (lags, rolling stats, calendar features), model training, and an interactive Dash dashboard
View ProjectCultural Fit Analysis
The candidate's project diversity, ranging from social gaming apps to academic research in distributed systems and personal AI-powered tools, indicates a broad interest and adaptability. Their ongoing Master's in Machine Learning and Data Analytics aligns well with an AI Engineer role, showing a commitment to continuous learning and specialization. The Flutter development background, while not directly AI, shows a strong engineering foundation.
Soft Skills & Operational Fit
The candidate demonstrates problem-solving and teamwork skills through project descriptions and a leadership role. Their experience in Agile environments and contributing to code reviews suggests good operational fit. The academic research paper indicates strong analytical and critical thinking abilities.