Remote ML Platform & Infrastructure Lead in London

Remote ML Platform & Infrastructure Lead in London

London Full-Time 60000 - 80000 £ / year (est.) No working from home possible
iProov

At a Glance

  • Tasks: Lead the ML platform and infrastructure, ensuring scalable and secure systems for model deployment.
  • Company: iProov, a pioneering biometric identity company with a science-based approach.
  • Benefits: Remote work flexibility, competitive salary, and opportunities for professional growth.
  • Why this job: Make a real impact by enabling ML Engineers to deploy models confidently.
  • Qualifications: Experience in MLOps, strong understanding of ML lifecycle, and collaboration skills.

The predicted salary is between 60000 - 80000 £ per year.

i Proov, a science‑based biometric identity company, is hiring a senior MLOps/platform engineer to lead the ML platform, infra and operations.

The role focuses on scalable, secure systems for model training, deployment and monitoring, enabling ML Engineers and Data Scientists to ship models with confidence.

You will own end‑to‑end ML lifecycle tooling, governance and production readiness, collaborating across ML, Data, Software and Platform Engineering.

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Remote ML Platform & Infrastructure Lead in London employer: iProov

iProov is an exceptional employer that champions diversity and inclusion, creating a supportive environment where every employee can thrive. With a hybrid work model based in the vibrant WeWork Waterloo in London, employees enjoy flexible working arrangements alongside competitive benefits, including a performance bonus and share options. The company prioritises personal growth and innovation, empowering its team to make meaningful contributions to cutting-edge biometric solutions that enhance security for clients worldwide.

iProov

Contact Details:

iProov Recruitment Team

We think you need these skills to ace Remote ML Platform & Infrastructure Lead in London

MLOps
Platform Engineering
Model Training
Model Deployment
Model Monitoring
End-to-End ML Lifecycle Tooling
Governance