Applied AI Engineer – Direct Acquisition

Overview

In this role you design and build AI agents that automate key business processes within iwoca’s Direct Acquisition team. You’ll lead system design and backend work, connecting agents to predictive models, data pipelines, and channel tools. You own end-to-end architecture, guardrails, and measurement to understand value and reliability. You’ll collaborate with product, commercial, and compliance teams to ensure safe, scalable AI-driven automation. This is a hands-on role with a strong emphasis on practical, production-ready AI systems that move fast and learn from results.

Pay / Benefits

• Flexible working hours
• Medical insurance from Vitality
• Private GP service for you and dependents
• 25 days holiday + birthday leave + buy/sell holiday option + unlimited unpaid leave
• One-month fully paid sabbatical after four years
• Employee equity incentive scheme

Responsibilities

• Design and build end-to-end AI agents that automate acquisition processes
• Architect reliable agent infrastructure including memory persistence, task management, access controls, and decision records
• Implement guardrails and compliance checks, budget limits, and permissions from the start
• Deliver end-to-end agent pipelines, integrating with predictive models, data pipelines, and channel tools
• Measure performance, failure modes, cost, and business value of agents
• Own design decisions and codebase, including AI tooling and production readiness
• Collaborate with product, commercial, and core systems teams to understand edge cases and ensure safe human-in-the-loop workflows
• Evaluate when to use agents versus iterative loops and write solutions that can be reused across processes

Key requirements

• System ownership of a complex production system
• Strong design judgement with ability to explain trade-offs
• Proven pace delivering in unfamiliar areas with stable operation
• Agent engineering experience with LLMs taking actions beyond chat/summaries
• Built-in controls: permissions, data access rules, audit trails
• Ability to constructively challenge ideas and propose better approaches
• Experience using AI coding tools to deliver engineering outcomes
• Clear written and spoken communication tailored to audience

• Constructive challenging of ideas
• Clear and concise communication
• Collaboration with cross-functional teams
• Agent engineering with LLMs taking actions
• AI coding tools usage and evaluation
• System design for persistence, memory, and task management

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