Position Summary: We are looking for a highly capable Android AI/ML Engineer - On-Device to help build intelligent, privacy-first mobile systems that can detect, respond to, and learn from dynamic real-world conditions. This role involves deploying resource-efficient ML models directly on Android devices, combined with backend integration for model management, telemetry, and secure update delivery. The ideal candidate has a strong background in on-device intelligence and cloud-integrated systems, especially in applications that require responsiveness, adaptability, and strict privacy controls. Key Responsibilities: Design, develop, and deploy on-device machine learning models optimized for Android, ensuring low latency and minimal resource consumption. Build robust and scalable ML pipelines using Android-native frameworks such as: TensorFlow Lite ML Kit (including GenAI APIs) MediaPipe PyTorch Mobile Build robust and efficient on-device data pipelines and inference mechanisms for real-time decision-making. Apply model optimization techniques such as quantization, pruning, and distillation for performance on mobile hardware. Ensure privacy-first design by performing all data processing and inference strictly on-device. Collaborate with backend teams to integrate with cloud-based model orchestration systems (e.g., MCP or similar) for: Model versioning, delivery, and remote updates Telemetry collection and model performance monitoring Rollout and A/B testing infrastructure Implement secure local storage, encrypted data handling, and telemetry pipelines that meet privacy and compliance standards. Support adaptive model behavior through on-device fine-tuning, personalization, or federated learning workflows. Skills: Technical Requirements: Proficiency in Android development using Kotlin and/or Java with deep understanding of app architecture, background processing, and system APIs. Hands-on experience with on-device ML frameworks: TensorFlow Lite, ML Kit, MediaPipe, PyTorch Mobile. Solid understanding of mobile performance optimization, including model size, memory usage, and latency. Proven ability to integrate Android apps with backend/cloud systems for: Model lifecycle management (delivery, updates, rollback) Logging, telemetry, and analytics Experience with secure Android development, including permissions, sandboxing, encryption, and local data protection. Strong understanding of privacy-first ML system design and local-only data processing. Preferred Qualifications: Experience working with model orchestration platforms (e.g., MCP, Vertex AI, SageMaker, or internal tools). Familiarity with federated learning, on-device personalization, or differential privacy. Background in building real-time, data-driven features in mobile apps at scale. Familiarity with cloud infrastructure (e.g., GCP, AWS) for ML model deployment and monitoring. Previous work in high-sensitivity domains such as identity, privacy, mobile security, or regulated industries is a plus. #J-18808-Ljbffr
Hire Talent