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 process.

Front the client's CTO and engineers on technical decisions; explain trade-offs in plain language and in depth when asked.

Watch the risks the account team flagged: licensing on new training data, engine-bridge capabilities, and multi-rule-set scope.

Skills (hands-on first)

Game AI / sequential decision-making : hands-on RL, imitation learning, or search-based agents (MCTS, self-play) - ideally for imperfect-information games (mahjong, poker, card games)

Expert

Python

for ML systems; strong software engineering (APIs, testing, CI)

Model training on gameplay data

end to end: data → training → evaluation → serving

LLM application engineering : reasoning layers, prompt and context design, structured outputs, guardrails

Low-latency inference : profiling, batching, caching, model-size trade-offs against a hard time budget

LLM observability and evaluation (Langfuse or similar)

AWS deployment for ML workloads





Technical leadership of a small pod; clear written and spoken communication with client engineers and executives

Knowledge

Game theory for imperfect-information games; evaluation of play strength (win rates, Elo-style ratings, baseline agents)

Game-engine integration patterns (event streams, action masks, state bridges)

Web3 / gaming product context — plus, not required

AWS Well-Architected for ML workloads

Experience Key characteristics (ideally 4/4):

Hands-on ML/AI engineering at production scale

Shipped an AI system inside a live product with hard latency limits

Cloud hyperscaler experience (AWS preferred)

Technology consulting / client-facing delivery background

Role-specific characteristics:

6+ years

hands-on ML/AI engineering, with real

game AI or sequential decision-making

work (RL / MCTS / self-play - not only LLM apps)

Trained models on user or gameplay data end-to-end (data → training → evaluation → serving)

Led small delivery teams while still coding personally

Comfortable owning an architecture in front of a technical client CTO

Questions for Applicants

Imperfect information : mahjong hides most tiles from each player. How does hidden information change your algorithm choice compared to a perfect-information game like chess?

Latency budget : tell us about a system you shipped with a hard response-time limit. How did you design, measure, and defend the budget?

LLM + model hybrid : how would you combine a trained game model with an LLM explanation layer so the explanation never contradicts the move?

Hands-on + lead : how do you balance personally coding the hard parts with leading an engineer and fronting the client?

#J-18808-Ljbffr

📌 AI Architect / Tech Lead (mahjong game) (Madrid)
🏢 Neurons Lab
📍 Madrid