Chief Technology Officer

About the role

We are looking for an experienced CTO who has built production-grade AI systems - not just shipped features on top of foundation models. You will inherit a working platform with real transaction volume, a 20-person engineering team, and an AI architecture that needs to evolve from workflow automation into fully autonomous commercial orchestration. Your mandate is to take a system that works and make it defensible, scalable, and increasingly self-improving.

What you'll do

• Design a modular, event-driven multi-agent framework where agents have well-defined scopes, shared memory, and coordinated execution.
• Move beyond prompt-chained LLM workflows toward tool-augmented, stateful agents that reason over real-time market data, inventory positions, credit exposure, and logistics constraints simultaneously.
• Architect feedback loops: agents that learn from trade outcomes, pricing performance, and fulfillment results to continuously update their decision logic.
• Build observability and evaluation infrastructure that makes agent behavior auditable, debuggable, and improvable.
• Architect algorithmic working capital allocation — real-time credit limit management, dynamic exposure modeling, and automated financing triggers embedded directly into trade execution.
• Develop a senior technical leadership layer with strong talents who own architectural domains, not just sprint tickets.
• Introduce clear ownership models: every system has an owner; every incident has an accountable team; every architectural decision has a record.
• Communicate the technical roadmap clearly at leadership and board level, including honest trade-off framing and risk visibility.

What you'll need

• 10+ years of engineering experience, with at least 5 in senior technical leadership at scale-ups or high-growth companies.
• Demonstrated experience building and operating multi-agent AI systems in production — not prototype or research contexts.
• Deep backend and distributed systems expertise: event streaming, microservices, API design, database scaling, observability stacks.
• Experience operating AI systems in regulated or high-stakes commercial environments where model behavior has financial or operational consequences.
• Track record of scaling engineering teams from 10 to 30+ people, including developing internal technical leadership.
• Experience designing and governing hybrid engineering models with external delivery partners.

Nice to have

• Familiarity with fintech infrastructure — credit systems, payment flows, or embedded finance.
• Hands-on familiarity with agentic frameworks (LangGraph, custom orchestration, or equivalent) and their production failure modes.
• Architectural fluency in streaming data, real-time inference, and feature engineering for production ML systems.