Sr. Android Engineer - AI/LLM

GSSTech Group, Bengaluru, India

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Published on 24 September 2026 · first appeared in our records on 24 September 2026.

Stable posting: first seen on 24 September 2026, with no abnormal reposting.

This posting shows no salary, while 3% of open postings in the same sector in this country (India) do.

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We are looking for an experienced  Android Engineer  to design, develop, and maintain large-scale, multi-module Android applications for digital banking products. The ideal candidate should have strong hands-on expertise in  Android, Kotlin, Jetpack Compose, Gradle, modular application architecture, performance optimization, and automated testing , along with practical experience integrating  LLM/AI capabilities  into production applications. The role requires someone who can work across complex Android architectures, optimize build and application performance, and contribute to AI-powered mobile experiences while maintaining strong engineering, scalability, reliability, and testing standards. Key Responsibilities • Design, develop, and maintain  large-scale, multi-module Android applications . • Build scalable and maintainable Android architectures across multiple feature teams. • Design and optimize complex  Gradle build architectures  and improve build performance. • Develop high-performance, responsive user interfaces using  Jetpack Compose . • Implement robust  Firebase Crash Reporting  and analyze crash and stability issues. • Diagnose and resolve  memory issues, ANRs, performance bottlenecks, and application stability problems . • Implement scalable dependency injection patterns using  Hilt/Dagger . • Develop highly testable and maintainable Android solutions across multiple modules and feature teams. • Integrate  LLM APIs  and AI capabilities into production Android applications. • Work with  Model Context Protocol (MCP)  frameworks and MCP-based integrations in production environments. • Implement and work with  token management and context handling  for LLM-powered applications. • Design and integrate  agentic AI workflows and multi-agent orchestration  where required. • Implement appropriate  AI error recovery and failure-handling strategies . • Collaborate with engineering, product, QA, architecture, and other cross-functional teams. • Contribute to code reviews, technical design discussions, engineering standards, and architectural decisions. • Balance architectural scalability with practical mobile constraints including performance, memory, reliability, and user experience. Core Technical Requirements Android Development • Strong hands-on experience in  Android development . • Strong proficiency in  Kotlin . • Experience developing large-scale and  multi-module Android applications . • Strong understanding of scalable Android architecture and modular design. • Advanced experience with  Gradle  and complex Android build systems. • Experience optimizing Android build performance. • Strong hands-on experience with  Jetpack Compose  and modern Android UI development. • Experience with  Firebase Crash Reporting/Crashlytics  and production crash analysis. • Strong experience diagnosing  memory issues, ANRs, and application performance problems . • Strong understanding and practical experience with  Hilt and/or Dagger  for dependency injection. AI / LLM / MCP Requirements Hands-on production experience with AI integration is required. Candidates should have experience with: • LLM API integration  within applications. • Working with LLM platforms such as  Claude, OpenAI, or similar platforms . • Model Context Protocol (MCP)  and MCP frameworks/tool servers. • Token management and optimization. • Context management and handling. • Agentic AI workflows . • Multi-agent orchestration . • AI error handling and recovery strategies. • Integrating AI-powered features into production applications. Additional Android Technical Skills Strong experience in at least  2–3  of the following areas: • Kotlin Coroutines & Flow • DataStore & Room • OkHttp & Protocol Buffers • WorkManager Testing & Quality • Strong understanding of Android testing and quality engineering practices. • Experience writing and maintaining automated tests. • Experience with automation testing frameworks and testable Android architecture. • Ability to build solutions with strong testability, maintainability, and reliability. • Experience identifying and resolving production defects and stability issues. Performance & Scalability • Experience with Android  memory profiling  and performance analysis. • Strong experience with  Firebase crash analysis . • Ability to diagnose  ANRs and application performance issues . • Experience optimizing large-scale Android applications. • Understanding of mobile performance constraints and resource management. • Experience working with scalable, modular Android architectures. GitHub & Engineering Profile Candidates with an active  GitHub portfolio  are highly preferred. The portfolio should ideally demonstrate: • Multiple Android projects. • Strong Android architecture and scalability. • Kotlin development. • Jetpack Compose. • AI/LLM integrations where applicable. • Clean and maintainable engineering practices. Additional strengths include: • Open-source contributions. • Technical writing. • Public technical projects. • Experience shipping AI-powered mobile features. Preferred AI / Technology Exposure Experience with one or more of the following will be highly valued: • Claude / Anthropic • OpenAI APIs • MCP tool servers • LLM-powered applications • Agentic AI systems • AI-powered mobile features • AI-assisted development workflows What We're Looking For We are looking for an engineer who can combine  strong Android engineering fundamentals with modern AI capabilities . The ideal candidate should be able to: • Think architecturally while remaining hands-on. • Work effectively within complex multi-module Android environments. • Build scalable and high-performance mobile applications. • Understand real-world mobile constraints. • Troubleshoot difficult production issues. • Work collaboratively across feature and engineering teams. • Learn and adapt to rapidly evolving AI technologies. • Translate AI/LLM capabilities into practical, production-ready mobile features. • Maintain a stro

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