{"id":1463746,"url":"https://alion.io/job/gss-group-senior-android-engineer-ai-modular-architecture","title":"Senior Android Engineer (AI & Modular Architecture)","company":{"id":5362,"name":"GSS Group","domain":"gsstechgroup.com","url":"https://alion.io/company/gsstechgroup","size_band":"201-500","is_staffing_agency":false,"employer_type":"staffing","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":83,"open_postings":10,"ghost_share":0,"stale_share":0.9,"repost_share":0,"time_to_fill_p50_days":15,"computed_at":"2026-10-22T06:10:10Z"}},"role":"Mobile","role_family":"Mobile","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"Claude","optional":false},{"name":"Crashlytics","optional":false},{"name":"Dagger","optional":false},{"name":"DataStore","optional":false},{"name":"Dependency Injection","optional":false},{"name":"Firebase","optional":false},{"name":"Gradle","optional":false},{"name":"Hallucination","optional":false},{"name":"Hilt","optional":false},{"name":"Jetpack Compose","optional":false},{"name":"Kotlin","optional":false},{"name":"Kotlin Coroutines","optional":false},{"name":"LeakCanary","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OkHttp","optional":false},{"name":"OpenAI","optional":false},{"name":"Room","optional":false},{"name":"WorkManager","optional":false},{"name":"Clean Architecture","optional":true},{"name":"Edge AI","optional":true},{"name":"Gemini","optional":true},{"name":"GitHub","optional":true},{"name":"Java","optional":true},{"name":"LiteRT","optional":true},{"name":"ML Kit","optional":true}],"status":"live","first_seen_at":"2026-09-29T13:55:05Z","employer_posted_date":"2026-09-29","last_verified_at":"2026-10-22T06:42:00Z","board_verified":true,"closed_at":null,"days_open":22,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":22},"description":"About the Role\nWe are looking for a Senior Android Engineer to build and scale large, multi-module Android applications used by a large and demanding user base. You will work at the intersection of mobile engineering and applied AI, owning build architecture, UI performance, app stability, and the integration of LLM driven features into production.\nThis role needs someone who thinks in systems but ships in sprints. You will make architectural decisions that affect several feature teams, while staying close enough to the code to diagnose an ANR at 2am or cut build times by half.\nKey Responsibilities\nArchitecture and Build Systems\nDesign and maintain scalable multi-module Android architectures that let multiple feature teams work independently without breaking each other.\nOwn the Gradle build setup, including convention plugins, version catalogs, build logic modularisation, and dependency management.\nOptimise build performance through configuration caching, build caching, parallel execution, and module graph tuning.\nDefine and enforce module boundaries, API contracts, and dependency rules across the codebase.\nUI Development\nBuild high performance, accessible UIs with Jetpack Compose.\nOptimise recomposition, state handling, and rendering performance for complex screens.\nContribute to and maintain a shared design system or component library.\nStability, Performance, and Monitoring\nImplement and maintain Firebase Crashlytics for crash reporting, alerting, and triage.\nDiagnose and resolve memory leaks, ANRs, jank, and startup performance issues with Android Studio Profiler, LeakCanary, Perfetto, and similar tools.\nSet up performance baselines and monitor regressions across releases.\nDependency Injection and Testability\nApply clean dependency injection patterns using Hilt or Dagger across modules.\nWrite testable code and maintain unit, integration, and UI test coverage.\nAI and LLM Integration\nIntegrate LLM APIs (Claude, OpenAI, or similar) into production mobile features.\nBuild and consume MCP (Model Context Protocol) servers and tools within production workflows.\nHandle token management, context window strategy, streaming responses, and cost control on mobile.\nImplement agentic workflows and multi-agent orchestration where the product requires it.\nDesign AI error recovery: timeouts, retries, fallbacks, hallucination handling, and graceful degradation when the model or network fails.\nBalance AI capability against real mobile constraints such as latency, battery, bandwidth, and offline behaviour.\nCollaboration\nWork closely with product, design, backend, and AI/ML teams to deliver features end to end.\nMentor engineers, review code, and raise the technical bar across feature teams.\nDocument architectural decisions and share knowledge internally.\nMandatory Requirements\n6+ years of native Android development in Kotlin.\nProven experience with large scale, multi-module Android applications.\nStrong Gradle expertise, including build optimisation and custom build logic.\nProduction experience with Jetpack Compose.\nHands-on experience with Hilt or Dagger.\nFirebase Crashlytics implementation and crash analysis experience.\nProven ability to diagnose memory issues, ANRs, and performance bottlenecks.\nHands-on production experience integrating LLM APIs.\nWorking experience with MCP frameworks or tool servers.\nSolid understanding of token management, context handling, agentic workflows, and AI error recovery.\nTechnical Expertise (at least 2 to 3 of the following)\nKotlin Coroutines and Flow\nJetpack DataStore and Room\nOkHttp and Protocol Buffers\nWorkManager\nPreferred Qualifications\nActive GitHub profile with multiple Android projects demonstrating clean architecture and scalability.\nOpen source contributions to Android or AI tooling.\nTechnical writing: blogs, conference talks, or internal engineering documentation.\nExperience shipping AI powered features to real users at scale.\nFamiliarity with on-device ML (ML Kit, TensorFlow Lite, Gemini Nano) is a plus.\nExperience in banking, fintech, or other regulated environments is a plus.\nWhat Success Looks Like in the First 6 Months\nMeasurable reduction in build times across the project.\nReduced crash rate and ANR rate on key user journeys.\nAt least one AI powered feature shipped to production with proper monitoring and fallback 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