AI Agents Solutions Architect - Finance

The Team

The Opportunity

Responsibilities

• span five core areas:
• Assess and map Finance operations
• Evaluate how financial work currently flows across the organization, including close, reconciliations, treasury, reporting, controls validation, and audit preparation.
• Identify capability gaps, manual bottlenecks, and integration failure points across systems including NetSuite, BlackLine, Kyriba, Fireblocks, and Lukka.
• Benchmark current operations against best-in-class practices and emerging AI capabilities to build the Finance automation roadmap, prioritized by operational leverage and regulatory risk.
• Work directly with accounting, FP&A, treasury, and finance operations teams to translate process pain points into buildable automation requirements.
• Architect the agentic finance platform
• Design the long-term architecture for an AI-native finance operating system. Define how agentic systems interact with financial infrastructure, data pipelines, and control and reporting frameworks.
• Evaluate Kraken's existing finance systems stack from first principles, including platforms and the interfaces and data flows between them. Identify opportunities to simplify the stack, reduce manual intervention, and define a scalable target architecture.
• Build an agentic finance layer that runs alongside the existing stack today, automating workflows while defining the migration path toward a more scalable, AI-native architecture over time.
• Build and deploy production finance automation
• Deploy production-grade agentic workflows that automate finance operations: reconciliations, close processes, audit and reporting preparation, treasury monitoring, financial analytics, and controls validation.
• Build using modern AI tooling including Claude and Anthropic APIs, Python-based orchestration, n8n or equivalent workflow engines, and MCP or similar agent coordination layers.
• Integrate internal and external systems by connecting APIs, data sources, and tools into unified automated workflows that deliver real operational leverage.
• Maintain human oversight where appropriate. Design for auditability and control integrity, not just efficiency.
• Own AI governance for Finance
• Establish guardrails that allow automation and agent behavior to operate safely in a SOX-regulated financial environment. Define which controls are maintained, which are redesigned, and how workflow scope preserves control integrity.
• Build frameworks covering data classification, auditability, human-in-the-loop checkpoints, failure detection, rollback mechanisms, regulatory compliance, and audit-ready design documentation.
• Classify all automation builds by risk tier before work begins. No build goes to production without a named Process Owner, documented data flows, access controls, and audit logging confirmed.
• Serve as the Finance domain representative in Kraken's Automate Everything Center of Excellence, contributing to governance standards and cross-functional build alignment.
• Build the foundation for AI-native Finance
• Create reusable frameworks, workflow templates, and documentation so that future Finance team members can safely build on top of the platform without relying on a single specialist.
• Train finance professionals on AI-assisted tools and workflows deployed in production. Drive adoption and operational independence, not just delivery.
• Build metrics, dashboards, and performance frameworks to measure efficiency gains, cost reduction, risk outcomes, and ROI from automation and AI initiatives.
• Provide leadership reporting on operational improvements, risk posture, and the finance platform roadmap.

You Should HODL

• This role is designed for a technical builder and systems architect who has already deployed agentic systems in production.
• Proven track record designing, deploying, and scaling agentic AI systems in production environments used by others, not just experimentation or internal demos.
• Deep understanding of LLM orchestration, multi-agent system design, and the failure modes of autonomous AI workflows in high-stakes operational environments.
• Strong systems architecture capability: able to move from discovery to design to build to deployment to optimization without relying on external consultants.