Catena
Head of Applied AI
Remote - United States · Exec
Sponsorship not specifiedDetected 216 days ago
TypeScriptPythonAWSMachine LearningLLMsAgentic AILangGraphAI Orchestration
About the role
- This role combines AI engineering and data science.
- The AI Platform team sits alongside Product Development and Banking Core, and reports to the CTO.
- Product Development relies on your team's capabilities, and you'll ensure AI integrates reliably with Banking Core via APIs, MCP, and other interfaces.
Responsibilities
- Own the orchestration layer connecting product experiences to banking infrastructure
- Partner closely with Product Development and Banking Core to ensure AI capabilities are deeply integrated
- Hire and develop AI engineers and data scientists as the team grows
- You build things directly. You're not looking for a role where you manage without coding.
- Strong intuition for model selection, eval methodology, and build-vs-buy tradeoffs
- You'll build systems directly while setting technical direction for how AI works across Catena, both in our products and our operations.
- We care more about good judgment on what to build than rigid adherence to categories.
- You'll build on an existing foundation while growing the team.
Requirements
- Deep experience shipping production AI systems, whether that's a decade in ML or fewer years with intensive focus on the current LLM paradigm
Nice to have
- Experience in fintech, payments, or regulated financial services
- Hands-on work with agent orchestration frameworks
- Background in compliance or risk systems where auditability matters
- You'll have the opportunity to work on challenging problems that have never been solved before, with a team that's defining an entirely new category of financial services.
- This is more than a job
- Your work will directly enable millions of AI agents to participate safely in commerce, unlocking trillions in economic value and increasing prosperity around the world.
This listing is sourced directly from Catena's careers page and normalized into a canonical job model.