For health, fintech, messaging & EU-regulated apps

Android Expert in On‑Device & Hybrid AI

The engineer companies call when AI has to run on the device. Local LLMs — Gemini Nano, Gemma — and hybrid on-device + cloud architectures that pass security review. On-device first, cloud only when it has to.

Work with me
8 yrs Android engineering
20+ Apps shipped
Millions Of users served
−76% AI cost, cloud-only → hybrid

Case studies & guides

Real numbers from shipping on-device AI on Android — written for the person making the call.

Talks

I speak about what I build — local LLMs on Android and the hybrid architectures around them. Live demos on real devices, not slides-only.

Dmytro Samoilov presenting device RAM limits for local LLMs on Android at the Cloudflare Lisbon office

Hybrid AI Architecture for Android: LLM On‑Device + Cloudflare Workers AI + MongoDB Atlas

Cloudflare Lisbon Office · Lisbon, Portugal

One AI feature, two backends: Gemma running locally on phones that can handle it, Cloudflare Workers AI catching the rest. Live demo on real hardware — a Samsung flagship doing speech-to-text fully on-device next to a Pixel 4a routed to the cloud. Same experience, different backend.

Invite me to speak

Work with me

I'm taking on a few companies that want help shipping private, on-device AI. Three ways to start:

Feasibility Audit

Start here — a fixed-scope review of your app, your device base, your data constraints. What you get:

  • Analysis of your codebase, feature set, and real device base
  • A clear call per feature: on-device, hybrid, or cloud
  • Cost math — local models vs cloud APIs at your scale
  • Device targets: RAM floor, chipsets, what to test on
  • The answers your security review will ask for

from €6,500 Fixed scope · roadmap in 2 weeks

Team Workshop

One or two days with your Android team, hands-on, on your codebase. The knowledge stays in your team.

from €2,400 / day 1–2 days · on-site or remote

Hands-on Integration

I build the feature with you: model selection, device-capability routing, fallback rules, benchmarks on real hardware.

from €750 / day Embedded with your team

Not sure whether on-device is even worth it for your app? Message me anyway — that conversation is the part I enjoy most.

Get in touch

What I'm building

Executive Scribe in progress

A note-taking Android app for high-profile executives who take privacy seriously. Voice is transcribed and summarized fully on-device, offline — data never leaves the phone.

Follow progress on YouTube →

Claude Code Template for Android

Open-source template for using Claude Code with Android projects. Configs, prompts, and workflow automation.

View on GitHub →

On-Device Image Generation (iOS)

iOS app using Apple Intelligence to generate images locally. No cloud API — generation happens entirely on the device.

View on App Store →

About

Dmytro Samoilov

I've spent eight years building Android apps — 20+ projects, millions of users, the full stack from architecture to performance.

Now I work on the question behind every AI feature: where does the user's data go to make this work? For health, fintech, and messaging apps — anything with GDPR in the room — that question decides whether the feature ships at all.

My answer is on-device first. Gemini Nano, Gemma, hybrid on-device + cloud architectures — I test these on real devices, benchmark what actually works, and document everything. Not theory. Real code, real numbers.

I build in public and share what I learn on YouTube and LinkedIn — the parts that worked and the parts that didn't. I also take it on stage: most recently a talk on hybrid AI architecture for Android at Cloudflare's Lisbon office.

Based in Lisbon.

Watch the build

I test on-device AI on real Android projects and film what I find — the parts that worked and the parts that didn't.

Building an AI feature that can't leak user data?

Tell me what you're building — I'll tell you what can run on-device, what needs hybrid, and what it will cost at your scale. Wherever is easier for you, I read everything myself.