**Job Description**

The core product experience runs on an LLM\-driven agent that extracts data from client\-submitted receipts and documents over WhatsApp, proposes accounting entries, and answers staff questions inside the web app. We’re hiring an AI/Agent Engineer to take ownership of this entire layer, making it cheaper, safer and more reliable while closing specific security gaps (prompt injection, unchecked tool authorization) identified in a recent technical audit. You will work alongside a backend\-leaning Fullstack Engineer, who owns the surrounding application and infrastructure.

**Responsibilities**

• Own the LLM integration layer used for document extraction (OCR), accounting\-entry proposals, and the in\-app chat agent, currently built on Anthropic/OpenAI APIs
• Implement prompt caching to cut redundant token usage. An identified \~83% cost\-reduction opportunity and introduce per\-tenant usage budgets and quotas
• Harden the agent against prompt injection: treat all extracted document text as untrusted input, and require explicit confirmation gates before any tool call that grants data access, creates a contact, or moves money
• Replace the agent’s currently hard\-coded (effectively disabled) permission model with real, enforced per\-tool authorization checks
• Evaluate and implement model tiering, cheaper models for extraction, stronger models for judgment calls and plan a fallback provider for resilience
• Build an evaluation/golden\-file test harness so model or prompt changes can be validated before shipping to production
• Implement PII redaction before data is sent to third\-party LLM providers, and partner with the Fullstack Engineer to log every agent tool call to an audit trail
• Advise on data\-processing\-agreement and data\-residency questions for the AI sub\-processors (Anthropic, OpenAI), alongside the company’s data\-protection advisor
• Contribute to the n8n\-side automation where it touches AI extraction, the document\-intake pipeline and coordinating handoffs with the Fullstack Engineer.

**Your Skills and Experience**

• 3\+ years of experience building production applications on top of LLM APIs (Anthropic Claude, OpenAI, or equivalent)
• Hands\-on experience with agentic tool\-use patterns, function/tool calling, multi\-step agent loops, not just single\-shot prompting
• Strong prompt\-engineering skills, including context management and techniques for defending against prompt injection
• Proficiency in Python or TypeScript, sufficient to build and modify a backend AI\-integration layer
• Experience building or using evaluation frameworks/golden\-file testing for LLM\-based systems
• Practical understanding of LLM cost structures and optimisation techniques, prompt caching, context trimming, model tiering
• Working knowledge of security concerns specific to LLM systems: prompt injection, data exfiltration via generated content, unsafe tool authorization
• Clear written communication in English; able to work asynchronously across time zones