About the project (description, duration, stage)
Hands-on

Tech Lead

for an

AI Companion

in an online mahjong game. The client is a

social gaming company (web3 element)

that scales its product and team. We deliver the AI side of their game as their embedded AI partner.

The AI Companion plays mahjong at a strong level and explains its moves. The core of the role is to

build the mahjong-playing algorithm : a dedicated decision-making model (RL, imitation learning, or search-based - trained on the client's hand-history data) with an

LLM reasoning layer

on top. Key design constraints: a

valid-action contract

with the game engine (the bridge supplies legal moves),

win detection , and a

2-second response budget

per move. Explanations run async. Support for more than one rule set (riichi and regional variants) is on the roadmap.

Duration : 3 months, 0.5 FTE.

What you'll actually do (example tasks)

Design and build the mahjong-playing algorithm: choose and defend the approach (imitation learning on hand histories, RL / self-play, search with MCTS, or a hybrid),



then train, evaluate, and ship it.

Own the technical architecture end to end: game model + LLM reasoning layer, valid-action mask, win detection, and the API contract with the client's game bridge.

Hit the

2-second response budget : design and measure the inference path, batching, and caching; keep a latency buffer for the client-facing number.

Define what data and event names we need from the client (hand histories, event streams); build the training and calibration pipeline on that data.

Build and run the evaluation harness: measure play strength against the client's reference points, and validate explanation quality.

Stand up

LLM observability with Langfuse

(async logging, N+1 batch) as an early sprint quick win.

Take over context from Vlad Borysenko (0.15-0.2 FTE supervision during ramp-up) and lead the sprint work with the AI Engineer; work with the client's Product Owner in a scrum pro

📌 AI Architect (Madrid)
🏢 Neurons Lab
📍 Madrid