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Crypto Finance Group, part of Deutsche Börse Group, provides professional digital asset solutions to institutional clients. The Group comprises Crypto Finance AG, regulated by FINMA in Switzerland, offering trading, custody, and wallet services, as well as Crypto Finance (Deutschland) GmbH, regulated by BaFin in Germany, offering trading and custody services. In January 2025, Crypto Finance secured a MiCAR license for the European market as one of the first providers in the EU. Crypto Finance AG is a SIX-approved crypto custodian for ETP issuers. For more information, please visit our website at About us - Crypto Finance We are currently looking for a AI Engineer. In the role, you will work closely with internal stakeholders, and the starting date is as soon as possible at our office on the 24th floor of the Prime Tower in Zurich Responsibilities AI & Automation Engineering Work with stakeholders across Compliance, Trading, Operations, Legal, Sales, and Finance to identify, scope, and prioritize use cases that genuinely move the needle. Engineer production data solutions: Deterministic automations, AI agents, RAG systems over internal documents, structured extraction pipelines. Build the firm's "innovation lab" environment where new use cases can be prototyped and evaluated. Maintain prompt and skill libraries as reusable, version-controlled assets, not as one-off scripts. AI Governance and Inventory Maintain the firm-wide inventory of AI systems and use cases, including those built outside D&A. Run the operational side of the company’s AI approval process: documentation, risk classification, model cards, evaluation artifacts. Policy is set at the executive level; you make sure the operational practice meets it. Conduct technical review of new AI initiatives proposed elsewhere in the company; advise on scope, risk, and design choices. Data Engineering and Platform Contribute to ELT pipelines on the Dagster + SQLMesh + dlt stack, primarily where AI & automation use cases require new data sources or transformations. Build the data substrate that AI workloads consume; feature views, document indexes, and structured event tables. Maintain infrastructure as code in Git with proper review and deployment standards. Requirements 3–6 years of relevant experience. We are flexible on title, could be AI engineer, analytics engineer with AI focus, or data engineer who has pivoted to AI. What matters is shipped work. Demonstrable production experience with LLM applications: at minimum structured extraction with LLMs, and agentic patterns (tool use, multi-step workflows). You can describe what failed and what you learned. Strong Python; comfortable building production ready code, not just notebooks. Working knowledge of evaluation discipline for LLM applications (eval sets, regression tests, observability), conceptual understanding of retrieval, and how to handle hallucination. Familiarity with at least one orchestrator (Dagster, Airflow, Prefect) and one transformation framework (SQLMesh, dbt). Solid SQL (window functions, joins, query design). Cloud experience, ideally GCP and BigQuery. Genuine curiosity about regulated environments and the discipline they require. Professional proficiency in English (German is a plus). Eligibility to work in Switzerland (Swiss permit or EU/EFTA citizenship). Equally important is how you think: You reach for the simplest thing that works. Boring SQL before vector search. Rules before agents. You can explain why. You are comfortable saying "this is not an AI problem" when the right answer is a dashboard, a process fix, or a deterministic script. You can run a stakeholder workshop and write production code in the same week. You take documentation, evaluation, and audit trails seriously, not as overhead. Nice to have: Prior exposure to regulated financial services, crypto, or comparable model-risk environments. W