Head of AI & Data Infrastructure

Build the global AI data infrastructure that powers the future of finance.

Financial data across wealth and asset management is fragmented among thousands of data sources, custodians, jurisdictions, asset classes, currencies, and proprietary formats. The role helps transform this data into trusted, standardized, token-efficient financial objects that AI models and intelligent applications can reliably understand and consume.

This is an opportunity to shape AI architecture, capabilities, engineering culture, and long-term technology direction while working closely with executive leadership and colleagues across the United States, Switzerland, Poland, and Singapore.

What You'll Build

• Define and execute the AI strategy and roadmap aligned with business objectives.
• Design and scale AI capabilities across the global financial data infrastructure and data lifecycle, including data connectivity, acquisition, normalization, reconciliation, validation, enrichment, and standardized AI-ready financial data object models.
• Own the architecture and delivery of AI and data infrastructure, from experimentation and production deployment to monitoring and continuous improvement.
• Establish AI architecture, MLOps, governance, security, and engineering standards for a regulated financial environment.
• Build, mentor, and scale a high-performing AI & Data Engineering team.

What Success Looks Like

Within the first months, you will:

• Define and launch the AI strategy and execution roadmap for a global financial data infrastructure and market standard.
• Deliver production-ready AI capabilities with measurable customer and business impact.
• Establish the AI development lifecycle, including MLOps, governance, evaluation, monitoring, and KPIs.
• Build the foundation of a high-performing AI and data infrastructure engineering team.

Requirements

• 5+ years of experience building AI and data infrastructure systems, software, data platforms, or cloud infrastructure, including 2+ years leading engineering teams or organizations.
• Proven experience designing and delivering production-grade AI and data solutions, including LLM applications, AI agents, RAG architectures, orchestration frameworks, and intelligent automation.
• Strong understanding of modern data architectures, distributed systems, cloud-native engineering, APIs, and backend technologies (Python).
• Practical experience with MLOps, Azure or another major cloud platform, Kubernetes, CI/CD, Infrastructure as Code, and observability.
• Strong leadership, communication, stakeholder management, and product mindset.

Nice to Have

• Experience in financial data, FinTech, WealthTech, wealth management, asset management, or another highly regulated industry.
• Experience building AI-native data infrastructure platforms or large-scale data integration systems.
• Knowledge of AI governance, security, privacy, auditability, and standards such as ISO 27001 or SOC 2.

How We Work

• Mission before ego.
• Builders over managers.
• Small, elite teams.
• Speed over bureaucracy.
• Ownership and accountability.
• The best idea wins, not the highest title.

Why Join

Work on a challenging engineering problem in financial technology and help shape the future of AI-powered global financial data infrastructure. Build from the ground up, work directly with executive leadership, and make a lasting impact on the technology, product, and organization.