Role Overview

We are seeking an experienced AI FullStack Engineer-II to 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.

Responsibilities

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.

Requirements

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.
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.