Mobile App Developer – AI Integration (Frontend/Backend, Offline-First)

Job Description

We are hiring a Mobile App Developer with expertise in building AI-powered applications that run fully offline on Android/iOS. This role combines strong frontend & backend mobile development skills with the ability to integrate quantized AI models, vector memory, and RAG pipelines into an app environment.

You will work closely with our AI engineers (LLM & TTS) and dataset team to bring optimized models into a functional, offline-first mobile app.

Responsibilities

• Design and develop a cross-platform mobile app (React Native, Flutter, or Native Android/iOS).
• Integrate quantized/distilled AI models (LLMs, TTS) into the mobile app environment.
• Implement local vector memory for personalized recall (SQLite + FAISS / Milvus-lite / GGML adapters).
• Build lightweight RAG pipelines that retrieve from local datasets and documents.
• Handle voice input/output (STT + TTS), real-time streaming, and conversational flows.
• Optimize performance: low-latency responses, minimal RAM usage, and battery efficiency.
• Create secure local storage systems for user data and memory (encrypted SQLite or secure file storage).
• Implement app backend logic (APIs, model wrappers, lightweight server where needed).
• Collaborate with AI engineers to test and fine-tune model deployment on device.
• Package and prepare MVP builds for testing and demonstration.

Mandatory Skills Checklist

✅ Mobile Development

• Strong experience with React Native, Flutter, or Native Android/iOS (Java/Kotlin, Swift).
• Knowledge of offline-first design (data sync, caching, secure storage).
• Hands-on with SQLite, Room DB, Core Data for persistent local storage.

✅ AI Model Integration

• Experience embedding AI models on device via llama.cpp / GGUF, ONNX Runtime, TFLite, or Core ML.
• Ability to wrap LLM inference into app APIs.
• Familiarity with vector databases / similarity search (e.g., FAISS, Milvus-lite, Weaviate-lite).
• Understanding of RAG pipelines (retrieval-augmented generation).

✅ Performance & Optimization

• Profiling mobile apps for latency, RAM, and battery impact.
• Implementing async pipelines for voice/text conversations.
• Knowledge of edge hardware accelerators (NNAPI, Metal, GPU, Qualcomm DSP).

✅ Backend/API Skills

• Comfortable setting up lightweight Node.js / Python (FastAPI, Flask) servers if needed.
• Secure handling of data, local-first design principles.

Nice to Have

• Experience with speech processing (STT/TTS integration).
• Prior work on AI chatbots, tutoring apps, or voice assistants.
• Familiarity with multilingual/localization (Urdu, Arabic, English).
• Knowledge of E2E encryption for secure memory storage.

Application Requirements

Applicants must include:

1. A short case study of a mobile app they built with offline AI integration (model used, framework, device tested).
2. A sample or repo link showing AI model or vector memory working inside a mobile app.
3. GitHub/portfolio or a short demo video.