AI Staff Engineer

Agentic Architecture & System Design

• Design and build the core agentic infrastructure for StavPay: agent orchestration, tool-use frameworks, memory/context management, and guardrails for autonomous financial workflows.

• Define the architecture for how LLMs interact with StavPay’s domain model: invoices/contracts/purchase orders, approvals, vendor records, payment instructions, payment orchestration, accounting entries - safely and reliably.

• Build and maintain MCP (Model Context Protocol) servers and integrations that expose StavPay’s capabilities as tools for AI agents and external AI platforms used by our clients.

• Design patterns for human-in-the-loop oversight, approval gates, and escalation paths in agentic financial workflows.

AI Feature Development

• Lead development of AI-powered product features: intelligent invoice processing, automated approval routing, anomaly detection, natural-language querying of financial data, and predictive cash-flow analysis.

• Build and iterate on prompt chains, retrieval-augmented generation (RAG) pipelines, and multi-step agent workflows tailored to financial operations.

• Implement evaluation frameworks: automated testing for AI outputs, regression detection, quality scoring, and production monitoring for model-driven features.

• Own the integration layer between LLM providers (Anthropic, OpenAI, etc.) and StavPay’s backend-model selection, fallback strategies, cost optimization, and latency management.

Technical Leadership

• Set technical direction for AI/agentic development across the engineering team. Write RFCs, architectural decision records, and technical specifications.

• Mentor engineers on AI integration patterns, prompt engineering, evaluation methodology, and safe deployment of model-driven features.

• Establish engineering standards for AI features: testing practices, monitoring, incident response, and responsible AI guidelines specific to financial data.

• Drive build-vs-buy decisions for AI tooling, frameworks, and infrastructure. Evaluate emerging tools and frameworks and make pragmatic adoption recommendations.

Cross-Functional Collaboration

• Partner with product management to identify high-value AI use cases, scope MVPs, and define success criteria grounded in client outcomes.

• Work with the implementation team to understand client workflows and pain points that AI can address.

• Collaborate with security and compliance to ensure AI features meet regulatory requirements for financial data handling, auditability, and data privacy.