Raphael Kim

AI Systems EngineerConcept → buildSoftware + hardware

AI Systems Engineer. From concept to working system.

I conceive, design and build AI-powered systems — connecting models, tools, software and hardware, from the first idea to a working product.

Voice systems at Snips / Sonos. Collaboration prototypes at SAP. Physical interaction research at MIT Media Lab. Today, I bring that experience to local AI, agentic workflows and connected objects.

Selected work

Four systems I conceived and built. Different interfaces, the same concern: make the whole loop work.

What I bring to a team

I work across the boundaries where a promising demo becomes a usable system: interaction, orchestration, inference, persistent state, embedded hardware and deployment. I can define an experience, make the technical choices and build the pieces that let someone actually use it.

The projects here originate in my own concepts and design work. I take them through implementation and iteration; professional contexts and team contributions are identified where relevant.

Built in different contexts

  • Snips / Sonos — voice and product prototyping. Contributed to the product definition and UX specification of Sonos Voice Control. Conceived and built contextual listening prototypes spanning interaction, lyrics retrieval, sensing and ESP32 firmware.
  • SAP — collaboration. Conceived the idea, built the prototype and developed the OS X application within the Chairman’s project team. The concept later evolved into CoScreen.
  • MIT Media Lab — physical interaction. Created SPrAyCE, a spray-based interface for drawing in mid-air, connecting a physical gesture to digital creation.
  • Independent R&D — AI systems. Design, build and operate the local inference, voice services, agent integrations and interfaces behind the projects in this portfolio.

How I build

Start with a use case. Make the whole loop work. Then find the weak boundaries: ambiguous requests, missing context, unavailable services, unsafe actions and outputs that cannot be verified.

I use models where interpretation or generation helps, and explicit software contracts where correctness matters. That can mean a source-backed answer, a reviewed diff, an event ledger or a hardware control loop that keeps working without the model.

My local setup

The local machinery behind my projects. One agent, a routing layer, and dedicated compute for reasoning, perception and voice.

LOCAL AI / SYSTEM MAP01 — 03
Telegram / terminal / services
/ ESP32 · voice endpoint
AGENT / AGENT01

Hermes

Plans, codes & calls tools

Dedicated VM · terminal & workspaces
ROUTING / SYNAPSE

The right capability

Intent, media assets & service APIs

LLM gateway · stable model endpoint
01 / REASONING

Spark cluster

2 × ASUS GX10
NVIDIA GB10

IA01ConnectXIA02
MODEL

Qwen 3.8
Flash Next

NVFP4 · SGLang · TP2

256K context
128 GB unified memory / node
02 / PERCEPTION

Multimodal node

Workstation
NVIDIA RTX 5090

IMAGEAUDIOVIDEO
MODEL

Qwen3-Omni
30B · A3B

NVFP4 · Thinker

Vision & media understanding
Available when the workstation is free
03 / VOICE

Speech node

MS03 · MS-02 Ultra
RTX PRO 4000 Blackwell SFF

SPEECH → TEXTTEXT → SPEECH
MODELS

Qwen3 ASR 1.7B
Qwen3 TTS 1.7B

Transcription · voice synthesis

Dedicated speech services
Independent of the multimodal node
Hermes reasons through the LLM gateway. Multimodal tools go through Synapse; transcription and speech use the dedicated audio services.
INFERENCE AT HOME