AI Engineer

• Identify, scope, and prioritize AI and automation use cases with stakeholders across Compliance, Trading, Operations, Legal, Sales, and Finance
• Engineer production data solutions, including deterministic automations, AI agents, RAG systems over internal documents, and structured extraction pipelines
• Build the firm s innovation lab environment for prototyping and evaluating new use cases
• Maintain reusable, version-controlled prompt and skill libraries
• Maintain the firm-wide inventory of AI systems and use cases
• Operate the AI approval process through documentation, risk classification, model cards, and evaluation artifacts
• Conduct technical reviews of new AI initiatives and advise on scope, risk, and design choices
• Contribute to ELT pipelines using Dagster, SQLMesh, and dlt
• Build data infrastructure for AI workloads, including feature views, document indexes, and structured event tables
• Maintain infrastructure as code in Git with review and deployment standards

• 3–6 years of relevant experience; shipped work is more important than title
• Demonstrable production experience with LLM applications, including structured extraction with LLMs and agentic patterns such as tool use and multi-step workflows
• Strong Python and ability to build production-ready code, not just notebooks
• Working knowledge of LLM evaluation discipline, including eval sets, regression tests, observability, retrieval, and hallucination handling
• Familiarity with at least one orchestrator:
  Dagster, Airflow, or Prefect
• Familiarity with at least one transformation framework: SQLMesh or dbt
• Solid SQL, including window functions, joins, and query design
• Cloud experience, ideally GCP and Big Query
• Genuine curiosity about regulated environments and their requirements
• Professional proficiency in English;
  German is a plus
• Eligibility to work in Switzerland, with a Swiss permit or EU/EFTA citizenship
• Ability to choose simple, appropriate solutions and distinguish AI problems from dashboard, process, or deterministic-script problems
• Ability to run stakeholder workshops and write production code
• Serious approach to documentation, evaluation, and audit trails
• Prior exposure to regulated financial services, crypto, or comparable model-risk environments is nice to have

Demonstrates expertise in building and maintaining AI and automation solutions, with a strong focus on production data engineering, LLM applications, and compliance within regulated environments. Proficient in stakeholder engagement, documentation, and the development of robust data infrastructure for AI workloads.

• Production Experience With LLM Applications
• Strong Python Programming
• Familiarity With Dagster Orchestrator
• Solid SQL Skills
• Cloud Experience With GCP

• Production Data Engineering
• Structured Extraction With LLMs
• Feature Views Development
• Version-Controlled Prompt Libraries
• Risk Classification
• Technical Review of AI Initiatives
• Documentation and Audit Trails
• Multi-Step Workflows
• Deterministic Automations
• AI Workload Infrastructure

• Stakeholder Engagement
• Curiosity About Regulated Environments
• Workshop Facilitation
• Problem-Solving
• Documentation Approach

• Regulated Financial Services
• Model-Risk Environments
• Compliance
• Crypto
• AI Systems Inventory

• Dagster
• SQLMesh
• Big Query
• Git
• AI Approval Process Tools