AI FullStack Engineer-II

Design and ship high-quality Android applications for consumer and enterprise audiences, while leveraging AI/LLM tools to build intelligent product features and accelerate development workflows.

• Build and maintain robust Android applications using Kotlin, Jetpack Compose, and XML layouts.
• Own end-to-end feature delivery — from architecture and UI to API integration, testing, and release.
• Integrate cloud LLM APIs (OpenAI, Anthropic, Gemini, etc.) into mobile apps to power intelligent, user-facing features.
• Build internal AI-powered developer tools — code assistants, smart documentation helpers, automated testing aids, and similar workflow accelerators.
• Design lightweight prompt engineering solutions and manage LLM API call lifecycles — error handling, retries, latency, and cost-awareness on the client side.
• Collaborate with backend, QA, design, and product teams in a structured enterprise environment.
• Contribute to reusable internal components or SDKs that make LLM capabilities easier to leverage across the team.

Skills Required

Android Developer role demands strong mobile engineering as the foundation, augmented with practical AI/LLM integration experience —

• Kotlin (must-have) — coroutines, flows, modern async patterns
• Jetpack Compose + XML layouts — hands-on with both
• Android architecture — MVVM, clean architecture, Jetpack components (ViewModel, StateFlow, Navigation, Room, WorkManager)
• Dependency injection — Hilt or Koin
• LLM API integration — calling and consuming OpenAI, Anthropic, Gemini or equivalent in production
• Prompt engineering basics — context management, token usage, cost tradeoffs
• RAG (Retrieval-Augmented Generation) — working knowledge of retrieval pipelines and when to apply them
• Knowledge base construction — familiarity with chunking, embedding, and indexing content for LLM consumption
• MCP (Model Context Protocol) — basic awareness of how tools, APIs, and data sources connect to LLM workflows

Ideal Profile

• 3–5 years of professional Android development with a portfolio of shipped consumer and/or enterprise applications.
• Hands-on experience integrating at least one AI-powered feature or developer tool into a real product or workflow.
• Strong understanding of Android performance, debugging, and release processes in a structured team environment.
• Practical knowledge of LLM concepts — prompts, context engineering, basic RAG, knowledge bases, and latency/cost tradeoffs.
• Familiarity with MCP and how it enables LLM-connected workflows.
• CI/CD experience, automated testing (unit + instrumentation), and comfort with enterprise-grade release processes.