Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Manage ML systems and collaborate with teams to enhance patient care.
- Company: Leading healthcare AI company focused on improving patient outcomes.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Fast-paced environment with a strong focus on teamwork and impact.
- Why this job: Make a real difference in healthcare by deploying innovative AI solutions.
- Qualifications: Experience in ML workflows, AWS, and continuous integration practices.
The predicted salary is between 36000 - 60000 £ per year.
A healthcare AI company in the UK is seeking an ML Ops Engineer to manage the infrastructure and lifecycle of machine learning systems. You will collaborate with data and clinical teams to ensure reliable deployment and monitoring of predictive models, which are crucial for patient care. This is a fast-paced environment requiring strong experience in ML workflows, AWS deployment, and continuous integration practices. Join us to impact patient outcomes positively.
Healthcare ML Platform Engineer for Edge & Production employer: Circadia Health
Contact Detail:
Circadia Health Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Healthcare ML Platform Engineer for Edge & Production
✨Tip Number 1
Network like a pro! Reach out to professionals in the healthcare AI space on LinkedIn or at industry events. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those related to healthcare. This gives potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for technical interviews by brushing up on your ML workflows and AWS deployment knowledge. Practice common scenarios you might face in the role to boost your confidence.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Healthcare ML Platform Engineer for Edge & Production
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with ML workflows and AWS deployment. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about healthcare AI and how you can contribute to improving patient outcomes. Let us know what excites you about this role!
Showcase Your Collaboration Skills: Since you'll be working closely with data and clinical teams, mention any past experiences where you’ve successfully collaborated across different departments. We love team players who can bridge gaps!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates from our team!
How to prepare for a job interview at Circadia Health
✨Know Your ML Workflows
Make sure you brush up on your knowledge of machine learning workflows. Be ready to discuss specific projects where you've managed the lifecycle of ML systems, especially in a healthcare context. Highlight how your experience aligns with the company's focus on patient care.
✨AWS Deployment Expertise
Since the role requires strong AWS deployment skills, prepare to talk about your experience with AWS services. Have examples ready that showcase how you've successfully deployed and monitored ML models in production environments. This will demonstrate your technical proficiency and readiness for the role.
✨Collaboration is Key
This position involves working closely with data and clinical teams, so be prepared to discuss your collaboration skills. Share examples of how you've effectively communicated and worked with cross-functional teams to achieve project goals, particularly in fast-paced settings.
✨Continuous Integration Practices
Familiarise yourself with continuous integration and deployment practices relevant to ML Ops. Be ready to explain how you've implemented these practices in previous roles, and how they can enhance the reliability of predictive models in healthcare settings.