---
title: 'AI Architect / Tech Lead (mahjong game) at Neurons Lab'
canonical: 'https://feeny.ai/job/ai-architect-tech-lead-mahjong-game-neurons-lab-poland-n3hs5h6ks68q'
type: 'job'
last_seen: '2026-09-08'
---

# AI Architect / Tech Lead (mahjong game) at Neurons Lab

- **Company:** Neurons Lab
- **Location:** Poland
- **Employment:** contract
- **Work type:** remote
- **Posted:** 2026-08-26
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/neurons-lab.com/ba2e112b-ed5f-41ff-9646-e60a7e6f1907

## Job description

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?

## About Neurons Lab

## Company Overview
- **One-liner**: Neurons Lab is an AI engineering partner that helps Financial Services organizations adopt AI and build production-grade agentic systems, from initial use case definition through deployment and continuous delivery.
- **Entity Type**: Private (Bootstrapped / Self-funded boutique firm)
- **Headquarters**: London, United Kingdom (International House, 64 Nile Str, London, N1 7SR)
- **Founded**: 2019
- **Founders**: Igor Sydorenko (CEO & Co-Founder) and Alex Honchar (CTO & Co-Founder)

## Core Business
- **Primary industry/industries**: AI Engineering & Consulting, focused exclusively on Financial Services (banking, capital markets, wealth management, insurance)
- **Target customers**: B2B — Fortune 500 financial institutions, large banks, wealth management firms, insurance companies, and adjacent regulated industries
- **Mission or purpose statement**: To help Financial Services organizations move from AI-curious to AI-enabled by building capability and deploying systems across core workflows — translating AI ambition into secure, scalable solutions that deliver commercial value.

## Products & Services
- **Custom AI Agents**: Deploy custom AI agents from discovery through production, enabling automation across core workflows with full auditability, traceability, and governance controls built in from the start. Decision logic is aligned with business rules and policies, designed for high-value workflows where accuracy, compliance, and scale are critical.
- **AI Training & Enablement**: Build AI fluency across teams, from engineering to the C-suite, through hands-on training grounded in the client's core workflows. Separate tracks for business and technical teams, with governance, compliance, and risk embedded from the start.
- **Continuous AI Delivery**: Post-deployment support with Forward-Deployed Engineers working alongside client teams to ensure systems continue to evolve. Governance, monitoring, and performance tracking are built in from the start, with AI adoption compounding across workflows over time.

## Market Standing
- **Valuation/Market Cap**: Not publicly available (private, self-funded boutique firm)
- **Key Metric**: Total Funding — Not disclosed (bootstrapped); the team reports 100+ AI implementations since 2019, including with Fortune 500 firms, and a team of 50+ AI engineers, architects, and analysts across Europe
- **Notable Investors/Partners**: Anthropic (partner), AWS (Advanced Partner, among first 15 to achieve AWS GenAI Competency in Agentic AI), Google Cloud (Partner)
- **Growth Signals**: Achieved AWS AI Competency in the Agentic AI category; named clients include HSBC (aligned 50+ senior leaders on AI deployment and governance) and Visa (scaled marketing operations across 9+ markets with LLM-based content system); works with leading Asian banks on agentic AI assistants for relationship managers

## Competitive Advantages
- **FSI Domain Expertise**: Deep specialization in Financial Services (banking, capital markets, wealth management, insurance) means no translation layer between business intent and technical execution. Compliance, governance, and risk are built into system design from the start.
- **Embedded Co-Creation**: Unlike traditional consultancies, they co-create with client teams through embedded delivery, working alongside internal stakeholders to transfer knowledge and expand impact from inside the organization.
- **Accelerated Delivery**: Pre-built FSI agent components and proven deployment patterns allow delivery of working, production-grade systems in weeks rather than months.
- **Boutique Senior Teams**: Small, senior teams (50+ engineers/architects) rather than large junior-heavy teams, enabling high-quality, fast execution.

## Strategic Focus
- Expanding agentic AI adoption within regulated financial environments, focusing on production-grade systems that operate within existing governance frameworks
- Building on partnerships with Anthropic, AWS, and Google Cloud to deliver cutting-edge AI solutions
- Deepening presence in wealth management, retail/corporate banking, private banking, SME banking, and insurance segments
- Scaling through a two-tier talent model (Core Team + Talent Network) to remain agile while growing

## Why Work Here
- **Remote-first culture**: Work from anywhere, with flexible hours — "design your day" philosophy
- **Benefits**: Unlimited PTO, flexible schedule, work on projects incorporating the latest AI technology (Anthropic, AWS, GCP)
- **Culture**: 7 guiding principles including ownership, genuine connections, and leadership development. Small, senior teams with high autonomy and low bureaucracy
- **Growth**: Opportunity to work on cutting-edge agentic AI for Fortune 500 financial clients, with clear paths to leadership roles
- **Team structure**: Two collaboration options — Core Team (full-time, benefits-eligible) or Talent Network (project-based, minimum 0.33 FTE)
- **Interview process**: Fast, streamlined — application → screening interview → validation interview → qualification interview → reference check

## Sources
1. [neurons-lab.com](https://neurons-lab.com/about/)
2. [neurons-lab.com](https://neurons-lab.com/)
3. [neurons-lab.com](https://neurons-lab.com/careers/)
4. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/neurons-lab.com)
5. [neurons-lab.com](https://neurons-lab.com/careers/core-team/)

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