ai engineer in iGaming

Block Labs is a technology studio focused on Web3, Artificial Intelligence, and iGaming. It develops high-scale, production-grade platforms, including autonomous multi-agent AI systems, decentralized financial infrastructure, and high-frequency iGaming platforms.

• Build and own analyst agents end to end, including Slack-native agents that translate business questions into governed SQL, answer executive P&L questions, post health briefings, and explain metric movements;
• Extend agents into customer-facing use cases with intent triage, RAG responses, conversational state machines, localized brand voice, and escalation logic;
• Build the risk-stratified tool layer between agents and back-office APIs;
• Harden agents against prompt injection, tool misuse, and data exfiltration;
• Build agents up the autonomy ladder using LangGraph, the Anthropic Agent SDK, MCP, or equivalent frameworks;
• Engineer feedback loops, decision audit logging, and evaluation harnesses;
• Build and productionise models for churn, lifetime value, bonus sensitivity, player risk, collusion, bot-play, multi-accounting, and treasury and payment anomalies;
• Ship models as governed signals across real-time, near-real-time, and batch tiers;
• Own multi-vector withdrawal risk scoring with cited rationale, confidence, and evidence-aware aggregation;
• Codify business rules with domain owners, simulate and backtest rule changes, design holdouts and control groups, and run deep-dive analyses;
• Build supervisor and approval surfaces for agents, including review queues, action proposal cards, session replay, and grading modules;
• Design and ship decision audit views, agent performance dashboards, risk review queues, and KPI views;
• Participate in design reviews and own domain decisions.

• 4+ Years of experience in software, data science, or machine learning engineering;
• 1+ Year building LLM-powered agents in production;
• Experience with tool use, function calling, structured outputs, retrieval, memory, and multi-step orchestration;
• Experience shipping a production RAG system and understanding grounding, chunking, retrieval quality, hallucination control, and refusal logic;
• Ability to defend customer-facing agents against prompt injection, tool-call abuse, and data leakage;
• Production ML lifecycle ownership, including feature engineering, training, serving, monitoring, and retraining;
• Statistical rigour in experiment design, holdouts, control groups, uplift measurement, and score calibration;
• Experience building evaluation harnesses and regression suites for non-deterministic systems;
• Strong Python, comfort with TypeScript, and strong SQL skills on columnar analytical databases;
• Ability to deliver stakeholder-ready review queues, approval interfaces, dashboards, and lightweight internal apps;
• Experience designing systems where model outputs feed deterministic execution;
• Experience with LLM observability and tracing;
• Nice to have: Experience in iGaming or other high-trust transaction-intensive environments, helpdesk or CS-platform integrations, blockchain or crypto-native transaction flows, constrained optimisation, bandits or reinforcement learning, rule engines or decision-management systems, Slack app development, Kafka or MSK consumers, idempotent processing, and failure handling.

• Fully remote with asynchronous-first communication;
• EU timezone overlap is preferred;
• Small, high-autonomy Intelligence team within the Data function;
• Reports to the Head of Data and coordinates with the AI, BI, Infrastructure, CS, and product teams;
• Architecture decisions are documented and debated;
• Models and agents start in propose-only, human-gated mode and graduate one level at a time;
• The platform is built for multi-tenant scale.