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Permutable

Real-time market signals via AI platform

Software Engineer - Backend & distributed systems

Backend EngineeringConfirmed live in the last 24 hours
Full-timeLondon, UKNo salary listed

About the Job

Requirements

  • 2–3 years of professional software engineering experience, or equivalent.
  • Expert proficiency in Python and strong software engineering fundamentals.
  • Experience with DevOps practices, including continuous integration and continuous delivery pipelines, containerization, automated testing, and monitoring.
  • A strong academic background in Computer Science, Engineering, Mathematics, or a related field, ideally from a top-tier university.
  • A proactive, resourceful, ambitious approach; eagerness to learn; and comfort working across multiple responsibilities in a fast-moving environment.

Responsibilities

  • Design and build robust software systems by developing scalable backend services and application programming interfaces for AI-driven applications.
  • Develop and optimize data pipelines by implementing efficient, reliable systems for ingesting, processing, and serving financial data.
  • Engineer cloud-native solutions on AWS, including Lambda, ECS/EKS, S3, and Redshift, and automate deployments with GitHub Actions and Pulumi.
  • Collaborate with the engineering team to share ideas, innovate, plan strategy, and decide on next steps.
  • Own assigned responsibilities and deliver code that shapes the core platform and reaches production rapidly.

Desired Qualifications

  • Hands-on experience with distributed systems, AWS services, or AI/large language model applications.

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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