About the project (description, duration, stage)

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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 Borys

📌 AI Architect (España)
🏢 Neurons Lab
📍 España