Android Developer for Audio Streaming and Offline LLM Integration

@AwaisChaudhry

Hello, I’m Muhammad Awais, and I can help you build a native Android app (Java) with real-time audio streaming and an offline on-device LLM experience. My approach covers a robust capture, low-latency streaming with buffering management and dropout resilience, plus optional playback of live or recorded streams using AudioRecord, AudioTrack, and ExoPlayer as needed. For the offline model run, I will enable on-device inference (using TensorFlow Lite or ONNX Runtime, with llama.cpp-based paths if suitable), focusing on speed, memory efficiency, and battery life, and integrating a clean UI with clear status indicators. The solution will be modular, testable, and well-documented, with debugging hooks for latency, audio glitches, threading, and background execution limits. I’ll provide a proof-of-concept demo early, followed by a production-ready implementation with a small, maintainable codebase and clear handoff. What is your preferred on-device ML runtime and target Android API level, and what are the maximum offline model size and latency constraints you expect? Best regards,

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in C Programming, Java, Mobile App Development, Android, Debugging, Performance Tuning and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.   Best regards, Sales department Tangram Canada Inc.     

@softsolution2000

I have extensive experience in C Programming, Java, Mobile App Development, Android, and Debugging, making me a perfect fit for the "Android Developer for Audio Streaming and Offline LLM Integration" project. I have successfully completed similar projects in the past where I integrated audio streaming and on-device ML inference using tools like TensorFlow Lite. I am confident in my ability to handle the challenges presented in your project and am eager to start working on it. Budget adjustments can be discussed later, and I am committed to delivering quality results within your budget. Please go through my profile to see my work history. Looking forward to discussing the job details and getting started on this project.

@patelvaibhav1997

Hello i have worked on such type of project on past it's available as review in my profile as well . i can finish your project batter as compared to other Thanks

@rbtech1984

Hello There!!! ⚜⭐⭐⭐⭐⚜(( Building a high-performance Android app with real-time audio streaming and offline LLM integration ))⚜⭐⭐⭐⭐⚜ Your project focuses on capturing microphone audio, streaming it with low latency, handling network drops, and optionally supporting playback, alongside fully on-device LLM inference with smooth UI integration. The goal is native Android performance without server dependency. I have hands-on experience developing real-time audio streaming apps using AudioRecord, AudioTrack, MediaCodec, and ExoPlayer, as well as integrating on-device ML models with TensorFlow Lite and llama.cpp. I can ensure minimal latency, efficient memory usage, and stable background execution. Key features I will implement: 1. Real-time audio streaming with robust buffering and dropout handling 2. Offline LLM inference fully on device with fast and efficient model execution 3. Smooth integration into the Android UI with performance and battery optimizations I am ready to deliver a production-ready solution and collaborate closely on testing and tuning. Warm Regards, Farhin B.

@toriquldev123

Hello Dear! I write to introduce myself. I'm Engineer Toriqul Islam. I was born and grew up in Bangladesh. I speak and write in English like native people. I am a B.S.C. Engineer of Computer Science & Engineering. I completed my graduation from Rajshahi University of Engineering & Technology ( RUET). I love to work on Web Design & Development project. Web Design & development: I am a full-stack web developer with more than 10 years of experience. My design Approach is Always Modern and simple, which attracts people towards it. I have built websites for a wide variety of industries. I have worked with a lot of companies and built astonishing websites. All Clients have good reviews about me. Client Satisfaction is my first Priority. Technologies We Use: Custom Websites Development Using ======>Full Stack Development. 1. HTML5 2. CSS3 3. Bootstrap4 4. jQuery 5. JavaScript 6. Angular JS 7. React JS 8. Node JS 9. WordPress 10. PHP 11. Ruby on Rails 12. MYSQL 13. Laravel 14. .Net 15. CodeIgniter 16. React Native 17. SQL / MySQL 18. Mobile app development 19. Python 20. MongoDB What you'll get? • Fully Responsive Website on All Devices • Reusable Components • Quick response • Clean, tested and documented code • Completely met deadlines and requirements • Clear communication You are cordially welcome to discuss your project. Thank You! Best Regards, Toriqul Islam

@devbaloni1983

Hello, Hope you're doing well! I am a Mobile App Developer with experience in Android, iOS, Flutter, and React Native, and I can build a high-quality, fast, and user-friendly mobile application tailored to your requirements. Why Choose Me 1. Experience in Android (Java/Kotlin) and iOS (Swift) 2. Cross-platform app development using Flutter/React Native 3. Expertise in UI/UX, responsive screens, and smooth navigation 4. Experience with Firebase, APIs, Auth, Push Notifications 5. Clean, optimized, and well-structured code 6. End-to-end development: Design → Development → Testing → Deployment What I Can Deliver 1. Custom Android & iOS mobile application 2. Login/Signup (Email, Google, OTP, Social Login) 3. API integration (REST/GraphQL) 4. Payment gateway integration 5. Admin Panel (if required) 6. Real-time features (Chat, Notifications, Location) 7. Bug fixing, UI redesign, performance optimization 8. Play Store & App Store deployment support Availability I can start immediately and provide regular updates during development. Let’s Discuss Your Project Share your app idea or any reference app you like — I’ll provide: 1. Best technical approach 2. Timeline 3. Cost estimate Looking forward to working with you!

@shushmobiweb6

Hello I HAVE WORKED ON REAL-TIME ANDROID AUDIO & ON-DEVICE AI SYSTEMS BEFORE! With 8+ years of experience, I understand exactly what you’re building: a native Android application where low-latency audio streaming and fully offline LLM inference must coexist without compromising performance, stability, or battery life. Your biggest risk isn’t feature scope—it’s managing threading, buffering, and resource constraints so audio and AI both feel instant and reliable. → Stable real-time audio: efficient mic capture, streaming, buffering, and dropout recovery with tight latency control → True offline intelligence: on-device LLM inference with careful memory, CPU, and battery optimization—no server dependency → Production-grade Android engineering: clean Java architecture, precise threading, and deep debugging around audio glitches and background limits The combination of AudioRecord / AudioTrack pipelines with local ML runtimes requires disciplined architecture. I focus on separating audio, networking, and inference workloads so each runs smoothly without blocking the UI or each other. Performance tuning, profiling, and edge-case handling are treated as core requirements, not afterthoughts. I’m also mindful of long-running background execution, lifecycle management, and device variability—critical when dealing with live audio and local models on Android. Best regards, Sushma S.

@technologiesit

As an experienced Android developer with a solid background in Java, I believe I have the skills you are seeking for your project. Combining my expertise in native Android app development along with my familiarity in handling audio streaming and offline LLM integration, I am confident in delivering a robust solution to meet your unique requirements. During my 10-year tenure in freelance web development, I have actively evolved my skill set to cater to the dynamic demands of the field. My team's diverse expertise works to our advantage as we are not only developers but problem solvers. We have engineered successful projects across various domains which is testament to our adaptability and commitment to quality deliverables. In terms of technical proficiency, I am extensively familiar with the Android audio APIs like AudioRecord, AudioTrack, MediaCodec as well as media players like ExoPlayer. My knowledge extends beyond audio streaming to include offline AI solutions such as ONNX Runtime and TensorFlow Lite. Employing these cutting-edge tools skillfully, I am confident in developing an efficient on-device LLM experience that performs optimally while minimizing battery usage and optimizing memory management. I value your project just as much as you do and pledge a 100% dedication towards its success. Let's collaborate to turn your vision into reality- ensuring quality deliverables that not only satisfy but also exceed your expectations.

@prashjain92

Hello, I see you want a Native Android app with real-time audio streaming and an offline LLM experience. The project requires capturing microphone audio, streaming it efficiently, handling buffering and network dropouts, optionally supporting live or recorded playback, and integrating on-device AI for fully offline inference. The focus is on a smooth, low-latency audio experience, seamless UI integration, and optimized performance for speed, memory, and battery. I have over 6 years of experience developing high-performance Android applications, including real-time audio streaming and on-device ML integration. I have completed multiple projects where I implemented low-latency audio pipelines using AudioRecord, AudioTrack, MediaCodec, and ExoPlayer, and integrated offline ML models using TensorFlow Lite, ONNX Runtime, and llama.cpp-based solutions. I ensured proper threading, background execution, and latency management, and delivered clean, well-documented code with full deployment instructions. For your project, I can deliver a fully functional APK demonstrating real-time streaming and offline AI features, along with the complete code repository and setup instructions. I can also advise on optimizations for model inference, audio buffering, and performance tuning to meet your goals. I provide three months of post-delivery support and would be happy to share relevant project examples in a private chat. Best regards, Prasham Jain

@zherukha1

Hello, I am excited about the opportunity to help you build and integrate real-time audio streaming features and an offline LLM experience in your native Android app. With over 6 years of experience in native Android development, I have successfully implemented audio features using APIs like AudioRecord and AudioTrack, ensuring efficient streaming and robust handling of network conditions. In my previous project, I developed an app that integrated live audio streaming while maintaining optimal performance through thorough debugging and performance tuning. I utilized libraries such as ExoPlayer and TensorFlow Lite for seamless local model inference, enhancing the app's capabilities without relying on server dependencies. I am confident in my ability to address latency issues and audio glitches, ensuring a smooth user experience. I would love to connect further to discuss your project needs and how I can contribute.

@billybryan98

Hello, I hope you are doing well. I’m an Android developer specializing in Java, with hands-on experience delivering real-time audio streaming and fully offline AI on-device. I design robust architectures, efficient threading, and clean UI integration to minimize latency and power use. I’ve built apps that capture microphone input with AudioRecord, stream with low-latency buffering (using ExoPlayer or native pipelines), and gracefully recover from network drops. For on-device AI, I’ve integrated TensorFlow Lite and ONNX Runtime, and explored llama.cpp-style local models, with careful memory tuning and performance profiling to keep latency low and battery usage down. I can handle end-to-end development—from mic capture to offline LLM integration—delivering a stable, production-ready solution with strong debugging and background execution handling. Please feel free to contact me so we can discuss more details. I am looking forward to the chance of working together. Best regards, Billy Bryan

@dushyant317

Hello, I’ve carefully reviewed your project requirements and clearly understand the tasks involved. I have 13 years of experience and strong expertise in the exact skills this project requires. I have successfully delivered similar projects before and can share relevant samples if needed. I will complete this within your expected timeline while maintaining quality and clear communication. I look forward to working with you and contributing sincerely to your project’s success.

@habiba8730

Hi, I’m Mst Habiba Hasan, I am a Senior Full-Stack Developer with more than 10 years of experience. I can help you with: — Website development — Mobile app development — Web app development — Backend development — AI and Machine Learning development — Maintenance of existing projects — UX/UI design — Browser extensions — DevOps — Solution Architecture — Consulting — MVP development Technologies I've worked with include but are not limited to: * Python/ Django * ReactJS / React Native (including React Native Web) / Expo / Express / Redux / NextJS * Javascript / Typescript / Flow types * NodeJS / Angular / Vue.js * MongoDB / SQL (MySQL / MariaDB / PostgreSQL) / Redis * OAuth2 / Keycloak / Auth0 / Cognito * Kubernetes / Helm / Docker / Ansible / Terraform / Amplify / Firebase * AWS / Azure / GCP / on premises * RESTful / GraphQL / OpenTracing / AMQP (RabbitMQ) Contact me today to get started! I’m excited to collaborate and bring your vision to life. Best regards, Mst Habiba Hasan

@shivasankari06

Hello, I am an Android developer with strong expertise in audio streaming, performance optimization, and on-device AI/LLM integration. I have hands-on experience with Kotlin/Java, ExoPlayer, background playback, offline caching, and low-latency streaming, as well as running lightweight LLMs locally using optimized, quantized models for secure and fast offline inference. My focus is on clean architecture, stability, and efficiency — including CPU/RAM optimization, coroutine-based concurrency, crash-safe lifecycle management, and battery-aware processing. I ensure streaming and AI run smoothly together without blocking UI or degrading playback. I can deliver: • Robust streaming with offline capability • Background/notification playback controls • Secure offline LLM inference • Clean, scalable code with documentation • Performance profiling and optimization I work transparently with milestone-based delivery, regular updates, and maintainable code quality. I would be glad to discuss your requirements, target devices, AI model expectations, and roadmap to align on the best technical approach. Looking forward to collaborating with you. Best regards, Shiva

@mdshamim000

Hi, Expert Android dev here with hands-on experience in real-time audio streaming and on-device AI. ✔ Mic capture & low-latency streaming (AudioRecord, buffering, dropout handling) ✔ Playback via AudioTrack / MediaCodec / ExoPlayer ✔ Strong debugging of audio glitches, threading & background limits ✔ Offline LLM inference on device (TFLite / ONNX / llama.cpp-based setups) with performance tuning ✔ Clean UI integration + battery/memory optimization Happy to start with a small audio or offline-LLM module and scale from there. Let’s build this right.

@Erantkm

I would love to contribute my expertise to your Android development project. As an AI specialist, I have a deep understanding of Neural Networks, Reinforcement Learning, and Computer Vision. Specifically, I have a solid foundation in on-device machine learning (LLM) implementation using frameworks like TensorFlow Lite, which aligns perfectly with your needs. My extensive experience in handling audio APIs such as AudioRecord, AudioTrack, MediaCodec, and ExoPlayer will be invaluable for the audio streaming aspect of this app. In previous projects, I've developed real-time audio streaming features integrated within native Android apps. This involved handling buffering efficiently and dealing with network dropout scenarios. Similarly, I'm also familiar with offline LLM integration that ensures the app can function without server dependency. My approach includes not just clean UI integration but also performance tuning to maximize speed, minimize resource usage (memory and battery), and ensure smooth execution even under heavy load.