Mobile Development – Android- W2

Job summaryClient is looking for Android Engineer.Must Have:Strong experience as an Android Engineer.Good understanding of the Android Manifest file.Kotlin, Jetpack Compose and CoroutinesStrong knowledge of Android basics.Architectural design patternsGood hands-on coding skills.Basic knowledge of AI tools.ResponsibilitiesBuilding UI's connecting to serversCustomer facing appUnit testing (Mockito framework)Integration testing (internal tool - based out in cucumber). Qualifications:Experience: 2–5 years building and shipping Android apps in production; ownership of features end-to-end.Language: Strong Kotlin (incl. coroutines); solid grasp of OOP, SOLID, and pragmatic design patterns.Modern UI: Jetpack Compose (preferred) and/or strong XML UI skills; theming, accessibility, localization, multiple screen sizes.Architecture: Hands-on delivery using MVI (unidirectional data flow): intents/actions → reducer → state; clear state modeling; side-effects handled cleanly.Async & state: Coroutines + Flow/StateFlow, structured concurrency, cancellation, threading, and backpressure awareness.Dependency Injection: Production experience with DI (commonly Hilt/Dagger); scoping, component design, testability.Android fundamentals: Lifecycle, Navigation, background work (WorkManager), permissions, deep links, notifications.Data layer: Room and DataStore; caching strategies; offline/poor-network handling.Networking: REST integration (e.g., Retrofit/OkHttp—verify org-approved libs), auth/token handling, pagination, retries, robust error handling.Testing & quality: Unit tests for reducers/use-cases, ViewModel tests, some UI tests; CI-friendly builds; lint/static analysis usage.Debugging & performance: Profiling, crash/ANR triage, memory/leak awareness, performance tuning in Compose.Delivery practices: Git workflow, code reviews, refactoring, clear documentation/ADRs when introducing architectural changes. Good-to-have (modern engineering)Modularization: Multi-module Gradle, build optimization, feature modules.Security basics: Secure storage patterns, certificate pinning concepts, privacy-by-design.Observability: Structured logging, analytics/event schemas, crash reporting best practices.Bonus: AI / ML requirements (nice-to-have)On-device ML: Experience integrating ML Kit or TensorFlow Lite, model constraints (latency, memory, battery), and on-device privacy considerations.LLM features: Building AI-powered UX (summarization, search, assistants) via approved APIs; streaming responses, caching, fallbacks, and guardrails.Prompt + evaluation: Basic prompt design, regression/evaluation mindset (quality metrics, red-teaming, offline test sets).Responsible AI: Understanding of PII handling, data minimization, consent, and secure telemetry for AI features.