Android/Kotlin Engineer --NYC , NY (Onsite) Need Local Only

Basic understanding of the following:

Kotlin

Jetpack compose

Coroutine

AI

MVI and MVVM pattern

Unit testing - Mockk

Coroutine testing

UI tests (Espresso/Compose UI test)

Must-have requirements

• 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/State Flow, 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 (Work Manager), permissions, deep links, notifications.
• Data layer: Room and Data Store; caching strategies; offline/poor-network handling.
• Networking: REST integration (e.g., Retrofit/Ok Http verify org-approved libs), auth/token handling, pagination, retries, robust error handling.
• Testing & quality: Unit tests for reducers/use-cases, View Model 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.
• Proficient in iOS/Android Development as well as multiple design techniques
• Working proficiency in iOS/Android toolset to design, develop, test, deploy, maintain and improve software
• Strong understanding of Agile methodologies with ability to work in at least one of the common frameworks
• Strong understanding of techniques such as Continuous Integration, Continuous Delivery, Test Driven Development, Cloud Development, application resiliency and security
• Proficiency in one or more general purpose programming languages
• Working proficiency in a portion of software engineering disciplines and demonstrates understanding of overall software skills including business analysis, development, testing, deployment, maintenance and improvement of Software