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Permutable

Real-time market signals via AI platform

AI Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeLondon, UKNo salary listed

About the Job

Requirements

  • Strong Python and software engineering skills, with experience shipping AI or machine-learning systems into production.
  • Hands-on experience with large language models or natural language processing, including evaluation and the practical limits of model outputs.
  • Ability to own a technical problem by making design choices, writing maintainable code, testing it and supporting it after release.
  • Experience with cloud infrastructure, containers and production monitoring.
  • Clear communication and an interest in how global events affect financial markets.

Responsibilities

  • Build and improve large language model and natural language processing 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, continuous integration and continuous delivery, and workflow orchestration.
  • Work with engineers and market specialists to turn research questions into tested product capabilities.

Desired Qualifications

  • Experience with multi-agent workflows, model fine-tuning, Airflow, AWS, GitHub Actions, Pulumi or GPU workloads.

About the Company

Permutable

Real-time market signals via AI platform

Permutable AI provides real-time market intelligence for institutional investors by converting unstructured data—news, policy updates, supply-chain signals—into structured trading signals delivered via API, data feeds, and terminal interfaces. Its platform uses large language models and explainable AI to generate narrative intelligence, ESG and carbon-emissions datasets, and tools like Trading Co-Pilot and sector-wide analysis engines to visualize cross-asset relationships and track sentiment across commodities, FX and macro themes. It differentiates itself by combining narrative and ESG data with explainable AI to offer faster, more transparent insights than traditional research and data vendors. Its goal is to help hedge funds, asset managers and other institutions make quicker, better-informed investment and risk decisions at the intersection of finance and climate data.

Company size
11-50
Company stage
Early VC
Total funding
$2M
Headquarters
London, United Kingdom
Founded
2020

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