AI Architect – Tech Lead, Mahjong Game

🕒 vor 1 Monat

🇵🇱 Polen – Remote

⏳ Vertrag

🟠 Senior

🤖 KI-Ingenieur

👻 Geisterscore 27%

infoinfo

🗣️🇺🇸🇬🇧 Englisch erforderlich

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Neurons Lab

51 - 200 Mitarbeiter

🤖 Künstliche Intelligenz

🏥 Gesundheitswesen

⚡ Energie

💰 Corporate Round im 2022-10

Artificial Intelligence • Healthcare • Energy

Neurons Lab ist ein weltweit verteiltes AI-Forschungs- und Entwicklungsunternehmen, das Innovatoren im Bereich der Spitzentechnologie dabei unterstützt, die Entwicklung und Markteinführung datengestützter Produkte zu beschleunigen. Unser Team verfügt über Fachwissen in den Grundlagenwissenschaften, Full-Stack AI/ML-Engineering und Produktdesign. Diese seltene Kombination und der Zugang zu knappen Talenten ermöglichen es Neurons Lab, bahnbrechende Lösungen für Kunden in den Bereichen HealthTech und EnergyTech zu entwickeln. Neurons Lab arbeitet innerhalb eines proprietären Bereitstellungsrahmens, der auf das Innovationsumfeld zugeschnitten ist: harter Wettbewerb, enge Zeitpläne, kaum vorhandene Datensätze und die Notwendigkeit, neuartige Lösungen zu schaffen.

Beschreibung

• Design and build the mahjong-playing algorithm using imitation learning, reinforcement learning, search with MCTS, or a hybrid approach • Train, evaluate, and ship the mahjong-playing model • Own the end-to-end technical architecture, including the game model, LLM reasoning layer, valid-action mask, win detection, and API contract with the game bridge • Design and measure the inference path, batching, and caching to meet the 2-second response budget • Define required client data and event names, including hand histories and event streams • Build the training and calibration pipeline using client data • Build and run an evaluation harness for play strength and explanation quality • Stand up LLM observability with Langfuse using async logging and N+1 batch • Take over context from Vlad Borysenko during ramp-up and lead sprint work with the AI Engineer • Work with the client's Product Owner in a scrum process • Present technical decisions and trade-offs to the client's CTO and engineers • Monitor risks involving licensing of new training data, engine-bridge capabilities, and multi-rule-set scope

🎯 Anforderungen

• 6+ years hands-on ML/AI engineering experience, with real game AI or sequential decision-making work • Hands-on RL, imitation learning, or search-based agents such as MCTS and self-play • Experience with imperfect-information games ideally including mahjong, poker, or card games • Expert Python for ML systems • Strong software engineering skills, including APIs, testing, and CI • End-to-end model training on user or gameplay data: data → training → evaluation → serving • LLM application engineering, including reasoning layers, prompt and context design, structured outputs, and guardrails • Low-latency inference experience, including profiling, batching, caching, and model-size trade-offs • LLM observability and evaluation experience with Langfuse or similar • AWS deployment for ML workloads • Game theory for imperfect-information games • Evaluation of play strength using win rates, Elo-style ratings, and baseline agents • Game-engine integration patterns, including event streams, action masks, and state bridges • AWS Well-Architected knowledge for ML workloads • Hands-on ML/AI engineering at production scale • Experience shipping an AI system inside a live product with hard latency limits • Cloud hyperscaler experience, preferably AWS • Technology consulting or client-facing delivery background • Experience training models on gameplay data end-to-end • Experience leading small delivery teams while coding personally • Ability to own an architecture in front of a technical client CTO • Advanced English level required • Availability for part-time remote work • Openness to B2B contractor collaboration

🏖️ Vorteile

• Part-time, 0.5 FTE engagement for 3 months • Remote work • B2B (contractor) form of collaboration

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