New opportunity

AI ENGINEER – MOBILE APPLICATIONS

A4I Lab (IIIT-B x Microsoft) · Bangalore, India

About this role

The A4I Lab (a partnership between Microsoft and IIIT-B) is seeking an AI Engineer with mobile development expertise to build AI-powered applications for real-world deployments in sectors like education, healthcare, and accessibility. You will bridge the gap between mobile engineering and AI integration to create reliable, accessible, and offline-first user experiences. Responsibilities: Build and maintain mobile applications using Flutter, React Native, or native Android with Kotlin. Integrate AI services including LLM APIs, speech tools, and vision capabilities. Design offline-first mobile experiences suitable for low-connectivity environments. Manage app releases, deployments, and versioning workflows. Implement Firebase analytics, crash reporting, and push notifications. Develop multilingual and accessible mobile interfaces. Collaborate with AI engineers, designers, and partner teams. Required Qualifications: 4 to 7 years of software engineering experience, including production mobile applications. Proficiency in Flutter, React Native, or native Android development. Hands-on experience integrating AI or ML services into applications. Experience with Firebase services and release management workflows. Working knowledge of APIs, backend integrations, and collaborative engineering practices. Strong communication and teamwork skills. Preferred Qualifications: Experience building multilingual or Indic-language mobile applications. Familiarity with TensorFlow Lite, Core ML, or ONNX Runtime Mobile. Experience with accessibility-focused mobile development. Exposure to offline-first architectures and field deployments. This is a contract position through March 2027, with the possibility of extension.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

FlutterReact NativeAndroidKotlinLLM APIsSpeech toolsComputer VisionFirebaseTensorFlow LiteCore MLONNX Runtime MobileMobile DevelopmentOffline-first architecture

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Offline-First Mobile Architecture
The role requires designing applications that function reliably in low-connectivity environments. You must prioritize local data persistence, synchronization strategies, and graceful degradation of AI features when network access is unavailable.
AI Service Integration
You must bridge the gap between mobile front-ends and AI capabilities. This involves integrating LLM APIs, speech tools, and vision models, while potentially optimizing performance using on-device frameworks like TensorFlow Lite or ONNX Runtime.
Inclusive and Accessible Design
A core mandate is building multilingual and accessible interfaces. You should prioritize implementing mobile accessibility standards and localization strategies to ensure applications are usable by diverse populations in sectors like education and healthcare.
Production Release Management
Beyond development, you are responsible for the full lifecycle, including versioning, deployment, and monitoring via Firebase. You must ensure stability through crash reporting and analytics in real-world, large-scale deployments.

Likely challenges to think through

Balancing AI Latency and Mobile Constraints
Integrating complex AI models into mobile apps often creates performance bottlenecks. Prepare by demonstrating how you manage resource-intensive tasks, such as vision processing, without compromising battery life or UI responsiveness.
Deployment in Low-Connectivity Contexts
Designing for field deployments in sectors like agriculture or rural education presents unique technical hurdles. Prepare by discussing how you handle data conflicts, partial updates, and local caching in offline-first architectures.
Cross-Functional Collaboration
You will work with AI engineers, designers, and non-profit partners. The challenge lies in translating complex AI research into intuitive, accessible mobile workflows. Prepare by highlighting your experience in bridging technical requirements with user-centric design.

Practice your answers

Technical practice

Offline-first mobile architecture

What to coverExplain how you handle data synchronization and state management when a device has intermittent or no connectivity. Discuss strategies like local database caching (e.g., Room or SQLite), conflict resolution policies, and background sync mechanisms.

Integrating AI models on mobile devices

What to coverCompare the trade-offs between using cloud-based LLM APIs versus on-device inference using frameworks like TensorFlow Lite, Core ML, or ONNX Runtime. Address considerations regarding model size, latency, battery consumption, and privacy.

Mobile accessibility and localization

What to coverDescribe your approach to implementing WCAG-compliant mobile interfaces and supporting multilingual content. Suggest how to handle right-to-left (RTL) layouts and dynamic text scaling effectively within Flutter or React Native.

Firebase for production mobile apps

What to coverDetail how you utilize Firebase services beyond basic authentication, such as using Crashlytics for proactive debugging and Analytics for user behavior tracking. Explain how these tools inform your release management and versioning strategy.

Handling large media and AI payloads

What to coverDiscuss best practices for managing memory and performance when processing vision or speech data on mobile hardware. Focus on techniques like model quantization, efficient image processing pipelines, and asynchronous task execution.

Cross-platform vs. Native development

What to coverGiven the requirement for Flutter, React Native, or Kotlin, explain how you decide which framework is appropriate for a specific project. Suggestion: Focus on performance requirements, team expertise, and the need for platform-specific hardware access.

Behavioural practice

Collaborating with non-technical stakeholders

What to coverDescribe a situation where you had to explain technical constraints of AI integration to designers or partner teams. Focus on how you balanced user experience goals with the technical limitations of mobile hardware.

Managing production releases under pressure

What to coverProvide an example of a challenging app release or deployment cycle. Explain the steps you took to ensure stability, manage versioning, and handle unexpected issues post-launch.

Adapting to field deployment constraints

What to coverShare an experience where you had to optimize an application for a low-resource or low-connectivity environment. Detail the specific actions you took to ensure the application remained functional for the end user.

Resolving technical disagreements in a team

What to coverDescribe a time when you disagreed with a teammate on an architectural decision or technical implementation. Explain how you communicated your perspective, evaluated the trade-offs, and reached a consensus.

Sharpen your knowledge

Topics to prepare

Offline-First Mobile Architecture
Study strategies for data synchronization, local caching, and state management in low-connectivity environments. Exercise: Design a conceptual architecture for a mobile app that allows users to perform AI-driven tasks without an active internet connection, focusing on how to queue requests and sync data once connectivity is restored.
On-Device AI Optimization
Understand the constraints of running machine learning models on mobile hardware. Exercise: Review the documentation for TensorFlow Lite or ONNX Runtime Mobile and explain the process of model quantization and how it balances inference speed with accuracy for mobile deployment.
Mobile Accessibility and Multilingual UI
Focus on WCAG standards and internationalization (i18n) for mobile applications. Exercise: Create a checklist for ensuring a mobile interface is accessible to users with visual impairments and supports Indic-language scripts, as these are critical for the lab's mission.

Skills to revise

AI Service Integration
Practice connecting mobile frontends to cloud-based LLM APIs and vision services. Exercise: Build a small prototype in Flutter or React Native that sends an image to a vision API and displays the processed result, ensuring robust error handling for network failures.
Firebase Mobile Lifecycle Management
Master Firebase for production-grade app monitoring. Exercise: Configure a sample project to implement Firebase Crashlytics and Analytics, then simulate a crash to verify that the event is correctly captured and reported in the Firebase console.
Mobile Release and Versioning
Develop proficiency in managing the full app release lifecycle. Exercise: Outline a CI/CD pipeline strategy for a mobile application that includes automated testing, versioning, and deployment to app stores, reflecting the lab's need for reliable production releases.

Your preparation checklist

Prepare a portfolio of production-grade mobile apps
Select 2-3 mobile projects built with Flutter, React Native, or Kotlin. Be ready to explain your release management process, how you handled Firebase integration, and how you managed app versioning in a production environment.
Develop an offline-first architecture case study
Create a technical summary or diagram explaining how you would design an app for low-connectivity environments. Focus on local data synchronization, caching strategies, and how you would handle AI model inference locally using tools like TensorFlow Lite or ONNX Runtime.
Demonstrate AI integration expertise
Prepare a walkthrough of an application where you integrated LLM APIs, speech-to-text, or computer vision. Highlight how you optimized the user experience for latency and reliability when calling these services from a mobile device.
Draft examples of accessible and multilingual UI design
Gather evidence of your experience building accessible mobile interfaces or applications supporting multiple languages. If applicable, showcase work involving Indic-language support or adherence to accessibility standards like WCAG for mobile.
Formulate questions regarding field deployment and impact
Prepare thoughtful questions about the A4I Lab's specific non-profit partnerships, the typical constraints of their field deployments, and how the team balances cutting-edge AI research with the practical requirements of offline-first mobile engineering.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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