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Android Engineer

Android Engineer

Cozzera

9 years

0 Lacs

india

Posted:6 days ago| Platform:

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Skills Required

android mobile development ml ai design vision tensorflow model reliability data flow inference ui rendering quantization deployment latency optimization integration testing monitoring kotlin kit architecture capture onnx reinforcement personalization vr management network java python gradle git firebase pytorch

Work Mode

Remote

Job Type

Full Time

Job Description

Position Title:

Experience:

Location:

About the Role

mobile development and on-device AI

Key Responsibilities

Design, develop, and maintain Android applications integrating
vision, speech, and heuristic intelligence systems
.
Implement and optimize on-device ML models using
TensorFlow Lite
,
MediaPipe
, and Android ML Kit.
Build heuristic or rule-based pipelines that refine or post-process model outputs to improve reliability and UX.
Manage efficient data flow between camera/audio, ML inference, and UI rendering in real-time.
Collaborate with backend/ML teams on model quantization, deployment, edge optimizations.
Ensure performance across devices: low latency, memory optimization, battery efficiency.
Embed continuous integration, testing, and monitoring pipelines for AI-enhanced apps.
Keep up with emerging Android ML tech — NNAPI, GPU acceleration, Edge TPU, etc.

Required Qualifications

Bachelor’s or Master’s in CS, EE, or related field.
7–9 years of experience in native Android development (Kotlin & Java).
Proven integration of
TensorFlow Lite, ML Kit, or custom on-device ML models
.
Hands-on experience with
computer vision
(e.g. object detection, image classification) and
speech recognition
(ASR, TTS).
Familiarity with
heuristic / rule-based systems
and hybrid AI pipelines.
Strong knowledge of Android architecture components, coroutines, asynchronous processing.
Experience with
camera frameworks
(CameraX, Camera2) and
audio capture / processing
.

Preferred / Nice-to-Have

Experience with
multimodal systems
combining vision, speech, and contextual sensors.
Familiarity with
MediaPipe
,
ONNX Runtime
, or alternative inference engines.
Knowledge of
hardware acceleration
(NNAPI, DSP, GPU).
Exposure to
reinforcement learning
,
adaptive user models
, or personalization heuristics.
Experience in
privacy-preserving on-device ML
, real-time inference, or AR/VR (ARCore).
Deep knowledge of app performance, memory management, threading, battery & network optimization.

Tools & Technologies

Languages: Kotlin, Java, (Python for model prep)
Frameworks: TensorFlow Lite, ML Kit, MediaPipe, NNAPI
APIs: CameraX, SpeechRecognizer, AudioRecord, WorkManager
Tools: Android Studio, Gradle, ADB, Git, Firebase, Jira
ML Ecosystem: TensorFlow, PyTorch → TFLite, ONNX

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