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# Senior AI Engineer

- **Company:** Permutable
- **Location:** London, UK (Remote available)
- **Experience:** Expert
- **Salary:** £64,830.0
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Artificial Intelligence, Airflow, Amazon Web Services, Cloud Computing, Continuous Integration, Github, Python (Programming Language), Machine Learning, Software Engineering, Pulumi, Large Language Models, Multi-Agent Systems, Data Pipelines
- **Published:** September 26, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5898406399

## About the Role

* Strong Python and software engineering skills, with experience shipping AI or machine-learning systems into production.
 * Hands-on experience with LLMs or NLP, including evaluation and the practical limits of model outputs.
 * The ability to own a technical problem: make design choices, write maintainable code, test it and support it after release.
 * Experience with cloud infrastructure, containers and production monitoring.
 * Clear communication and an interest in how global events affect financial markets.
 * Experience with multi-agent workflows, model fine-tuning, Airflow, AWS, GitHub Actions, Pulumi or GPU workloads would be useful. We care more about what you have built and operated than whether you have used every tool in our stack.

## Description

We process information from global sources and turn it into structured, traceable signals used in institutional research and market workflows. We're looking for a Senior AI Engineer who can take language-model systems from experiment to reliable production use.

You'll work closely with our founder, engineers and researchers in a small London team. Your decisions will shape how our AI systems interpret incoming information, assess its relevance and deliver results that users can inspect and trust.

What you'll do

 * Build and improve LLM and NLP pipelines that extract entities, identify developing narratives and produce structured market intelligence.
 * Design evaluations for accuracy, consistency, latency and failure modes, then use the results to improve models and workflows.
 * Develop Python services and data pipelines that process information at scale and deliver it to production systems.
 * Deploy, monitor and maintain AI workloads using AWS, containers, CI/CD and workflow orchestration.
 * Work with engineers and market specialists to turn research questions into tested product capabilities.


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