We are building **Arqis, an AI-powered risk intelligence platform for AI infrastructure**, helping data center operators, GPU cloud providers, lenders, and insurers identify, quantify, structure, and transfer financial, commercial, and regulatory risks.

We are looking for a **senior AI Engineering Lead / Agentic AI Engineer** to lead the development of our next generation of AI agents, starting with a **Counterparty Risk Agent**.

Our regulatory and policy intelligence agent is already developed, and we are expanding into additional risk modules.

**Initial project: Counterparty Risk Agent**
- Build an autonomous AI agent to assess counterparty creditworthiness, financial health, ownership structures, commercial exposure, and default risks.
- Integrate structured and unstructured data from financial statements, contracts, corporate registries, and external intelligence sources.
- Develop automated risk scoring, evidence-backed assessments, continuous monitoring, and actionable risk recommendations.
- Integrate the agent with our existing platform and multi-agent architecture, including human-in-the-loop review and audit trails.

**Technical requirements**
- Strong experience building production-grade agentic AI systems, including LLM orchestration, tool calling, RAG, and multi-agent workflows.
- Proficiency in Python and TypeScript, with experience in Azure OpenAI, APIs, databases, and cloud infrastructure.
- Experience designing scalable backend architectures, evaluation pipelines, and reliable data integrations.
- Familiarity with financial services, credit risk, fintech, or enterprise risk intelligence is highly desirable.

**What we're looking for**

A highly independent, hands-on technical lead who can own architecture, implementation, testing, and deployment, rather than simply developing LLM prototypes. The initial focus is the Counterparty Risk Agent, with opportunities to lead subsequent agents covering GPU residual values, compute demand, pricing, and infrastructure financing risks.

Remote engagement, with preference for candidates in Asia or time zones overlapping Singapore and the Middle East. Potential for a longer-term leadership role based on performance.
