TL;DR

Machine Learning Engineer (AI Engineering): Build and maintain infrastructure and APIs for deploying machine learning models in production with an accent on scalable cloud deployments, CI/CD pipelines, and operational excellence. Focus on designing and implementing robust ML Ops workflows, integrating ML services, and ensuring reliability of cloud-based ML systems.

Location: Hybrid in London, United Kingdom

Company

%hirify_global% is an award-winning insurance company known for pioneering AI-enhanced underwriting solutions in collaboration with Google.

What you will do

Develop and maintain infrastructure for real-time and batch ML model deployment.
Build and maintain Python APIs (Flask/FastAPI) to serve ML models.
Collaborate with engineers to integrate ML services into applications.
Design and implement CI/CD pipelines for ML deployment.
Monitor and maintain cloud-based ML services for reliability and performance.
Contribute to model registry improvements and operational excellence.

Requirements

Location: Hybrid work based in London, UK
Bachelor's or Master's degree in quantitative field or equivalent experience.
Experience deploying and maintaining ML models in production.
Strong Python development skills and software engineering best practices.
Experience with Terraform, cloud platforms (GCP, AWS, Azure), Docker, and CI/CD.
Familiarity with Agile methodologies and ability to work independently.

Culture & Benefits

35-hour work week with hybrid working system.
25 days holiday plus 2 additional %hirify_global% days.
Health insurance, gym allowance, pension plan.
1-month sabbatical leave after 5 years of service.
Team spirit and open culture with many engagement opportunities.