AI Engineer

Join our Data & Analytics team at Crypto Finance, headquartered in the Prime Tower in Zurich. Your primary mandate is pushing the company’s AI strategy forward: identifying use cases across departments, engineering production-grade AI and automation solutions, and helping to build the firm's AI governance practice. You will also contribute to the data platform that underpins Finance, Operations, and Risk reporting, since AI work at our company sits directly on top of that platform.

This is a hybrid role for someone who is equally comfortable in a stakeholder workshop and an IDE. The work potentially spans AI agents and copilots, deterministic automation, RAG systems, and the data infrastructure they rely on. You will be expected to push back on bad use cases as much as you ship good ones.

What you will do:

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 is 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.