ML Ops Engineer: Production ML Pipelines & Edge Deployments

ML Ops Engineer: Production ML Pipelines & Edge Deployments

Full-Time 63000 - 77000 Β£ / year (est.) No working from home possible
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At a Glance

  • Tasks: Own and optimise production ML systems, building end-to-end pipelines and deployment infrastructure.
  • Company: Join Circadia Health, a leader in healthcare technology innovation.
  • Benefits: Enjoy competitive pay, flexible work options, and opportunities for professional growth.
  • Other info: Be part of a dynamic team focused on cutting-edge healthcare solutions.
  • Why this job: Make a real difference in healthcare by ensuring reliable predictive models.
  • Qualifications: Experience in ML operations and strong collaboration skills required.

The predicted salary is between 63000 - 77000 Β£ per year.

Circadia Health is seeking an experienced ML Ops Engineer to own the infrastructure and lifecycle of our production ML systems.

You will build and maintain end-to-end ML pipelines, deployment infrastructure, and monitoring to keep predictive models accurate and reliable across cloud and edge environments.

You will collaborate with ML, data, and clinical teams to ensure reproducibility, CI/CD for models, and robust observability while maintaining healthcare-grade security and privacy standards.

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ML Ops Engineer: Production ML Pipelines & Edge Deployments employer: Circadia Health

Circadia Health is an exceptional employer dedicated to transforming healthcare through innovative technology. With a strong focus on employee growth, we offer a collaborative work culture that encourages creativity and professional development, all while making a meaningful impact on patient care. Located in a vibrant area, our team enjoys unique advantages such as flexible working arrangements and access to cutting-edge resources.

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Contact Details:

Circadia Health Recruitment Team

We think you need these skills to ace ML Ops Engineer: Production ML Pipelines & Edge Deployments

ML Pipeline Development
Infrastructure Management
Cloud Deployment
Edge Computing
Monitoring and Observability
Collaboration with Cross-Functional Teams
CI/CD for Machine Learning Models