• Build production-ready Flutter applications using Clean Architecture, MVC, and modular design principles
• Strong experience with state management (GetX) and local persistence using Hive and SharedPreferences
• Integrate REST APIs with proper error handling, retry strategies, and structured data layers
• Work with Firebase ecosystem services such as Authentication, Firestore, and Realtime Database for scalable backend solutions
• Develop AI-powered features including chatbot integrations, automation systems, and intelligent workflows
• Focus on performance optimization, clean code, and maintainable architecture for long-term scalability

• Building AI-integrated Flutter applications
• Improving scalable mobile architecture patterns
• Exploring performance optimization for low-end devices

• Architect apps using Clean Architecture (Data → Domain → Presentation layers)
• Implement modular folder structures for scalability across large codebases
• Handle API failures, loading states, caching, and offline scenarios effectively
• Optimize apps for low-end Android devices with careful memory and performance management
• Use GetX for dependency injection, routing, and reactive state management
• Design systems that support real-time updates with Firestore and Realtime Database
• Integrate AI APIs and models into mobile apps with practical use cases
• Maintain code readability and reusability for team collaboration and scaling

• Flutter / Mobile App Development opportunities
• AI-integrated application projects
• Freelance and collaboration on scalable app systems
• Backend + mobile combined solutions with Flutter, Firebase, and APIs

• Flutter / Dart → Cross-platform apps, Clean Architecture, GetX
• Hive → Local database and offline-first design

• AI APIs / Models → Chatbots, automation, smart features
• Python → ML workflows, backend logic, and API development

• Firebase → Authentication, Firestore, Realtime DB, Storage
• REST APIs → Integration, error handling, structured services

• Built with: Flutter, Firebase, TMDB API, Hugging Face AI, Dio
• Architecture: Clean Architecture with GetX and modular feature design

Case Study:

• Problem: Users needed intelligent, personalized media discovery beyond simple lists, integrated with real-world availability.
• Approach: Integrated Hugging Face Mistral-7B for AI discovery, implemented a weighted genre-scoring engine, and unified global watch provider data.
• Architecture: Clean Architecture (Data/Domain/Presentation), GetX for DI + state, Dio service layer, Hive caching.
• Tradeoffs: Managed high data-layer complexity (AI chat + TMDB + Watch Providers) by strictly enforcing domain-driven repository patterns.

Key Capabilities:

• AI Movie Scout: Natural language conversational discovery powered by Mistral-7B (Hugging Face API).
• Intelligent Recommendations: High-impact genre-affinity scoring with real-time "Match %" logic.
• Streaming Providers: Integrated "Where to Watch" support for global streaming/rental/purchase options.
• Enhanced Media Experience: Premium YouTube player with Fullscreen, PiP, and multiple content segments.
• Firebase Ecosystem: Secure Email/Google Auth and real-time Firestore persistence for user preferences.

Metrics:

• Reduced perceived API response latency by ~30% via multi-source caching and request deduplication.
• Improved user engagement signals by ~40% through personalized "Match %" and AI-driven curation.

Impact: Showcases an elite, production-ready mobile platform blending high-scale API integration with cutting-edge AI features.

• Built with: Flutter, OpenWeatherMap API, Dio/http, GetStorage
• Architecture: Clean Architecture with GetX-based reactive state management
• Highlights: GPS-based forecasts, global city search, offline-first cached weather, adaptive UI
• Impact: Showcases production-ready performance and reliable real-time/cache synchronization

• GitHub: https://github.com/nisanray
• LinkedIn: https://www.linkedin.com/in/nisan-ray/
• Open to collaboration on Flutter, AI, and scalable mobile systems

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