Mobile Development – Android- W2

Job summary Client is looking for Android Engineer. Must Have: Strong experience as an Android Engineer. Good understanding of the Android Manifest file. Kotlin, Jetpack Compose and Coroutines Strong knowledge of Android basics. Architectural design patterns Good hands-on coding skills. Basic knowledge of AI tools. Responsibilities Building UI's connecting to servers Customer facing app Unit 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.