About Block Labs
Block Labs is a premier technology studio operating at the bleeding edge of Web3, Artificial Intelligence, and iGaming. We don't just ship features; we engineer high-scale, production-grade platforms that power the next generation of digital products. We are a collective of senior engineers, product strategists, and builders who refuse to compromise on architecture.
The Role
We are investing in governed AI agents that work directly with our customers and internal teams. Data & Intelligence now sits at the centre of several products we are developing, and the platform we have built is ready to carry more advanced intelligence: real-time decisioning, predictive modelling, and governed AI agents. As an AI Engineer on the Intelligence team, you are the product engineer of the data and agent platform. You build production agents that answer real business questions and, as they earn autonomy, act with real customers and real money: grounded, auditable, secured against adversarial input, and gated by human approval where the stakes demand it.
Key Responsibilities
- AI Agents: Build and own analyst agents end to end (Slack-native agents that translate natural-language business questions into governed SQL), extend into customer-facing agents (intent triage, RAG, multi-turn conversational state machines), build the risk-stratified tool layer between agents and back-office APIs, and build agents up the autonomy ladder using LangGraph, the Anthropic Agent SDK / Model Context Protocol (MCP), or equivalent orchestration frameworks.
- Machine Learning: Build and productionise the models behind the platform's decision signals (churn, lifetime value, bonus-sensitivity, risk scores, fraud/bot detection). Ship models as governed signals, not notebooks: versioned, SLA'd contracts with the decision engine and your agents.
- Data Science: Own multi-vector withdrawal risk scoring and codify business rules with domain owners to keep policy auditable.
- Dashboards & Surfaces: Build the supervisor and approval surfaces for your agents and design/ship the dashboards through which the business consumes your work.
How We Work
- Fully remote with asynchronous-first communication. EU timezone overlap is preferred.
- Small, high-autonomy Intelligence team within the Data function.
- Architecture decisions are documented and debated. You will participate in design reviews and own your domain decisions.
- Autonomy is earned by evidence.
- We build for multi-tenant scale from day one.
Culture
Mature, mission-driven, and low-ego. We value clarity over noise, outcomes over theatrics, and pace without chaos.
About You
- 4+ years of experience in software, data science, or machine learning engineering, including 1+ years building LLM-powered agents in production: tool use and function calling, structured outputs, retrieval and memory, and multi-step orchestration with frameworks such as LangGraph or the Anthropic Agent SDK.
- You have shipped a production RAG system and can talk concretely about grounding, chunking and retrieval quality, hallucination control, and when to refuse to answer.
- You treat customer-facing agents as an attack surface: you can explain how you would defend against prompt injection, tool-call abuse, and data leakage through model outputs.
- Production ML lifecycle ownership: feature engineering, training, serving, monitoring, and retraining.
- Statistical rigour: experiment design, holdouts and control groups, uplift measurement, and score calibration.
- Evaluation discipline for non-deterministic systems: you have built evaluation harnesses and regression suites.
- Strong Python for production services, comfort in TypeScript for the review and approval surfaces you will ship, and strong SQL skills on columnar analytical databases (ClickHouse preferred).
- Able to take your own work to a stakeholder-ready surface: review queues, approval interfaces, dashboards, and lightweight internal apps, using a front-end framework or tools such as Streamlit.
- Experience designing systems where model outputs feed deterministic execution, keeping that boundary clean (models score, suggest, and draft; governed logic decides), with LLM observability and tracing (Langfuse, LangSmith, or similar).
Nice to Have
- Experience in iGaming or other high-trust, transaction-intensive environments.
- Helpdesk or CS-platform integration experience (Intercom, Zendesk, or similar).
- Exposure to blockchain or crypto-native transaction flows.
- Experience with constrained optimisation, bandits, or reinforcement learning.
- Experience with rule engines or decision-management systems, and Slack app development.
- Event-driven and streaming experience: Kafka or MSK consumers, idempotent processing, and failure handling.
