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AI Data Residency Compliance Engineer

Leo Larsson

Full-time · Job · London

About the role

GDPR says European data stays in Europe. China's PIPL says Chinese data stays in China. India's DPDPA has its own rules. When you're running ML models that train on data from 40 countries, data residency compliance becomes an infrastructure problem that most teams solve by not solving it — they just avoid international markets. Our platform lets companies train and serve AI models globally while respecting every jurisdiction's data sovereignty rules. The technical challenge is building ML infrastructure where data never crosses the wrong border. That means federated training across regional compute clusters, model serving with geo-aware routing, and audit logging that proves compliance to regulators who ask for evidence. You'll architect the multi-region ML platform that makes this work transparently for our clients. The compliance engineering team is four people. You'll be the most senior, responsible for architecture decisions that have direct regulatory impact. When you get it wrong, our clients face fines — so we move deliberately and verify thoroughly.

Responsibilities

  • Architect multi-region ML infrastructure respecting data sovereignty requirements
  • Build federated training pipelines across geographically distributed compute clusters
  • Implement geo-aware model serving with data residency enforcement
  • Design audit logging systems that satisfy regulatory evidence requests
  • Create compliance verification tools for automated residency checking
  • Advise product teams on data residency implications of new features

Requirements

  • 6+ years in cloud infrastructure with multi-region architecture experience
  • Deep knowledge of Kubernetes, Docker, and multi-cloud deployment patterns
  • Understanding of data protection regulations: GDPR, PIPL, DPDPA, CCPA
  • Experience building audit and compliance systems for regulated industries
  • Strong system design skills for distributed ML infrastructure

Benefits

  • Fully remote with flexible hours
  • 30 days PTO
  • Private health and dental insurance
  • £4,000 annual conference budget
  • Stock options with 4-year vesting

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Categories

MLOps & AI Infrastructure AI Product & Strategy

Skills

python docker kubernetes aws gcp

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$130k – $160k / year

AI Expertise

MLOps & AI Infrastructure AI Product & Strategy

Skills

python docker kubernetes aws gcp azure

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