Artificial Intelligence Engineer

Ethiq; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Ethiq in London is hiring an AI Engineer to join a small, deeply technical team building enterprise AI agents that learn and automate enterprise processes. You will work on learning systems that ingest knowledge bases, conversations, tickets, and logs, producing reliable instructions with confidence scoring.

The role covers extending learning libraries, designing end-to-end LLM-powered workflows, and collaborating directly with enterprise customers to move ideas from prototype to production.

Qualifications

• 3+ years of professional experience with production LLM systems.
• Hands-on with agents and autonomous systems.
• Interest in human-in-the-loop feedback, prompt optimisation & context engineering.
• Able to explain work to engineers and customers alike.

Responsibilities

• Extend the core context learning library—ship with a customer first, then generalise it back into the platform.
• Design LLM-powered systems and agentic workflows from prototype to production.
• Build agents that autonomously edit and maintain large knowledge bases.
• Own the reliability layer: evals, confidence scoring, automate-vs-escalate logic.
• Architect async systems for complex AI orchestration.
• Work directly with enterprise customers, not through a ticket queue.

Skills

Production LLM systems

Agentic workflows

Context engineering

Explain work to engineers/customers

Tools

Python

LLM tooling

Async systems

Job description

AI Engineer — Series A enterprise AI startup (London)

Most AI deployments fail for the same reason: the model doesn't know how your company actually works.

I'm hiring for an elite team solving exactly that. They build agents that learn an enterprise's processes - ingesting knowledge bases, conversations, tickets and system logs, then producing instructions agents can execute, with confidence scoring that decides when to automate and when to hand back to a human.

This role sits on that core learning system.

What you'd actually be doing:

• Extending the core context learning library — ship with a customer first, then generalise it back into the platform
• Designing LLM-powered systems and agentic workflows from prototype to production
• Building agents that can autonomously edit and maintain large knowledge bases
• Owning the reliability layer: evals, confidence scoring, automate-vs-escalate logic
• Architecting async systems for complex AI orchestration
• Working directly with enterprise customers, not through a ticket queue

What they're looking for:

• 3+ years professional experience, with production LLM systems you can talk through in real engineering detail
• Hands-on with agents and autonomous systems
• Genuine interest in how systems improve over time — human-in-the-loop feedback, prompt optimisation, context engineering
• Comfortable moving between a research paper and a production codebase
• Able to explain your work to engineers and customers alike

Bonus points for scaling LLM workflows to millions of requests, multi-agent systems in production, or building evaluation frameworks for enterprise deployments.

The culture: small, deeply technical team, high hiring bar, and problems that often have no known solution. Engineers are expected to bring direction, not wait for it.