Hiscox

Insurance

PrincipalMachineLearningEngineer

£145–210k ~AI est. London, United Kingdom; York, United Kingdom FULL TIME
The Brief

“Principal Machine Learning Engineer at Hiscox. Skills: Machine Learning Engineering, MLOps, Production ML systems. Act as technical lead for Machine Learning Engineering. Technically lead complex ML systems”

Industry & Context.

Insurance
Problems you'll solve

Troubleshooting; Root cause analysis

What They're Looking For.

Must Have

Bachelor’s or Master’s degree in Computer Science, Engineering, or related quantitative field (or equivalent experience), Extensive experience as senior or principal Machine Learning Engineer delivering production ML systems at scale, Proven track record of owning or shaping ML platforms, MLOps frameworks, or critical ML infrastructure, Exceptional Python skills in machine learning engineering context, Software engineering fundamentals (OOP, testing, design patterns), Deep experience building, deploying, and operating production ML systems, Hands-on experience with major cloud platform (AWS, GCP, or Azure), Containerised deployments, Expert knowledge of MLOps and CI/CD, Working knowledge of SQL, Experience using AI-assisted coding tools in production engineering context

Nice to Have

Insurance or financial services experience

What You'll Do.

Act as technical lead for Machine Learning Engineering

Technically lead complex ML systems

Define and evolve production ML patterns

Lead deep technical decision-making

Contribute hands-on to critical systems

Define best practices for AI-assisted coding tools

Lead by example applying AI-assisted development techniques

Partner with teams to design

Shape platform standards and interfaces

Lead technical spikes and proof-of-concepts

Influence developer tooling choices

Ensure platform enables fast

Ensure ML systems meet standards

Define and implement frameworks for monitoring

Champion operational excellence across ML services

How You'll Work.

Team & Collaboration

Cross-functional environments; Data Science teams; Engineering teams; Platform teams; Value streams

Process & Methodology

CI/CD, Agile

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