Native iOS & Android AI Engineer - Swift & Kotlin - India

Native iOS & Android AI Engineer - Swift & Kotlin - India at Skarvo Your Location and Job You will work in a remote-first work environment from anywhere in India. We offer competitive salary. Founders of Skarvo previously worked at Apple as senior engineers and designers. They are graduates of MIT. Skarvo founders are located in Silicon Valley, California. The Role We're looking for a Mobile AI Engineer expert in Swift and Kotlin to build the runtime infrastructure that makes our on-device LLM an agent — the skill execution engine, native-to-WebView bridges, tool dispatch pipeline, and the chat UI that renders rich interactive AI outputs. You'll integrate pre-trained and pre-quantized models (Gemma family via LiteRT-LM on Android and MLX Swift on iOS), wire them to a modular skill system, and build the full agentic loop — all running locally on the device with zero cloud dependency. This is a live product with real users in 175 countries. You'll work directly with the founding team and ship features into existing Swift and Kotlin native codebases. Key Requirements 5+ years of professional mobile engineering experience Strong experience with both Swift/SwiftUI or Kotlin/Jetpack Compose, with expertise to work across both platforms Experience with async/concurrent programming — Kotlin Coroutines and Swift Concurrency (async/await, actors) Hands-on experience with on-device ML inference SDKs — Apple MLX / MLX Swift, Core ML, LiteRT-LM, LiteRT (TensorFlow Lite), MediaPipe, or ExecuTorch Experience with small language models for on-device inference — Gemma, Qwen, Bonsai, LFM, Ministral 3, and similar Experience building native-to-WebView bridges — WKWebView on iOS or WebView + JavascriptInterface on Android Strong JavaScript proficiency — async/await, DOM APIs, fetch, Canvas, Web Audio Understanding of LLM function-calling, tool-use, skills and how tool schemas interact with model inference Familiarity with model quantization tradeoffs and on-device memory/latency constraints (you select and benchmark models, not train them) Preferred Familiarity with agentic AI concepts — planning loops, function calling, tool use, multi-step reasoning Understanding of text embeddings, vector search, and semantic retrieval — ideally on-device using SQLite with vector extensions (sqlite-vec, SQLite-Vector), FAISS, or similar Experience designing RAG (Retrieval-Augmented Generation) pipelines — combining embedding models, vector indexing, and language model inference, ideally in an on-device or resource-constrained environment Familiarity with AI agent orchestration and infrastructure — systems that wire agents together, manage tool dispatch, memory, and multi-agent coordination, such as NullClaw, LangGraph, CrewAI, AutoGen, or similar — and understanding of how these patterns apply to on-device, resource-constrained environments Awareness of agentic protocols — MCP, A2A, AP2 Startup or high-growth company experience preferred Key Skills Track record of shipping on-device ML features on real devices — not just demos Track record of shipping complex mobile architectures involving embedded web engines, native bridges, dynamic UI rendering, and local model inference. Ability to design systems that span three async boundaries: model inference, native UI, and WebView JavaScript execution Systems thinking for agent architecture — tool registries, planning loops, state persistence, error recovery Performance intuition for on-device constraints — memory budgets, battery impact, context window limits, WebView lifecycle Ability to work autonomously on ambiguous problems Clear communicator who can explain architecture decisions to engineers and non-engineers Experience with AI development tools — Claude Code, Codex, Copilot — integrated into daily workflow Responsibilities Own on-device AI architecture — drive model selection, inference pipeline design, and build-vs-buy decisions for all AI features Own the agentic runtime end-to-end — model integration, function-calling pipeline, skill execution engine, and rich chat UI on both iOS and Android Integrate pre-trained models using Apple MLX / MLX Swift on iOS and LiteRT-LM on Android Design and build the AI skill system — a modular architecture where the LLM discovers, loads, and executes skills that extend its capabilities with JavaScript logic, interactive UI, and native device actions Build the native-to-JavaScript bridge — a sandboxed WebView execution environment with bidirectional communication on both platforms Implement the chat rendering layer — heterogeneous message types including embedded interactive WebViews, images, progress panels, and native action confirmations Architect the async orchestration pipeline that coordinates LLM inference, tool execution, WebView JS, and UI updates Ship on-device AI features — local AI chat, tool calling, semantic search, smart suggestions, and agentic capabilities on the product roadmap Benchmark and select models for Skarvo's on-device requirements — evaluate new Gemma releases, quantization variants, and inference configurations Build agentic AI systems — design and implement on-device agents that can plan, call functions, use tools, and act on behalf of users — all locally with no cloud calls Design on-device RAG pipelines — build local embedding, vector indexing, and retrieval systems that power semantic search and context-aware AI features Collaborate with iOS and Android engineers — integrate ML pipelines into the native Swift codebase using Core ML, MLX Swift, and Swift Concurrency on iOS, and into the native Kotlin codebase using LiteRT-LM (TensorFlow Lite) and Kotlin Coroutines on Android Stay current — evaluate new models, frameworks, and techniques as the on-device AI landscape evolves rapidly Tech Stack On-device inference (iOS): Apple MLX, MLX Swift, Core ML On-device inference (Android): LiteRT-LM, MediaPipe Models: Gemma family (Gemma 4 (E2B, E4B), FunctionGemma, EmbeddingGemma) iOS: Swift 6, SwiftUI, WKWebView, Swift Concurrency Android: Kotlin, Jetpack Compose, WebView, Kotlin Coroutines JavaScript: Vanilla JS, Web APIs, CDN library integration Agentic protocols: MCP, function-calling schemas Embeddings & search: sqlite-vec, on-device embedding models CI/CD: GitHub Actions Backend: Firebase (ephemeral relay only — zero server persistence)