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from architecture and UI to API integration, testing, and release.\n\n- Integrate cloud LLM APIs (OpenAI, Anthropic, Gemini, etc.) into mobile apps to power intelligent, user-facing features.\n\n- Build internal AI-powered developer tools - code assistants, smart documentation helpers, automated testing aids, and similar workflow accelerators.\n\n- Design lightweight prompt engineering solutions and manage LLM API call lifecycles - error handling, retries, latency, and cost-awareness on the client side.\n\n- Collaborate with backend, QA, design, and product teams in a structured enterprise environment.\n\n- Contribute to reusable internal components or SDKs that make LLM capabilities easier to leverage across the team.\n\nIdeal Profile :\n\n- 4 - 6 years of professional Android development with a portfolio of shipped consumer and/or enterprise applications.\n\n- Hands-on experience integrating at least one AI-powered feature or developer tool into a real product or workflow.\n\n- Strong understanding of Android performance, debugging, and release processes in a structured team environment.\n\n- Practical knowledge of LLM concepts - prompts, context engineering, basic RAG, knowledge bases, and latency/cost tradeoffs.\n\n- Familiarity with MCP and how it enables LLM-connected workflows.\n\n- CI/CD experience, automated testing (unit + instrumentation), and comfort with enterprise-grade release processes.\n\nSkills Required :\n\n- Android Developer role demands strong mobile engineering as the foundation, augmented with practical AI/LLM integration experience \n\n- Kotlin (must-have) : coroutines, flows, modern async patterns\n\n- Jetpack Compose + XML layouts - hands-on with both\n\n- Android architecture : MVVM, clean architecture, Jetpack components (ViewModel, StateFlow, Navigation, Room, WorkManager)\n\n- Dependency injection - Hilt or Koin\n\n- LLM API integration : calling and consuming OpenAI, Anthropic, Gemini or equivalent in production\n\n- Prompt engineering basics : context management, token usage, cost tradeoffs\n\n- RAG (Retrieval-Augmented Generation) : working knowledge of retrieval pipelines and when to apply them\n\n- Knowledge base construction : familiarity with chunking, embedding, and indexing content for LLM consumption\n\n- MCP (Model Context Protocol) - 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