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- Tasks: Lead the design of a next-gen ML platform and automate processes.
- Company: Innovative insurance tech firm in Greater London.
- Benefits: Competitive salary, flexible working, and opportunities for mentorship.
- Why this job: Shape the future of ML technology and make a real impact.
- Qualifications: 4+ years in production ML systems, especially with Google Cloud.
- Other info: Join a forward-thinking team with a focus on reliability and efficiency.
The predicted salary is between 48000 - 72000 £ per year.
A forward-thinking insurance technology firm in Greater London seeks a Senior Machine Learning Engineer to lead the design of its next-generation machine learning platform. The ideal candidate has over 4 years of experience in building production ML systems, especially in Google Cloud.
You will be responsible for:
- Automating processes
- Developing MLOps frameworks
- Mentoring team members
This role requires a deep understanding of Vertex AI and ML tooling, with a strong emphasis on reliability and efficiency.
Senior ML Engineer – MLOps Platform on Google Cloud employer: Policy Expert
Contact Detail:
Policy Expert Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior ML Engineer – MLOps Platform on Google Cloud
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already working in MLOps or at companies you admire. A friendly chat can open doors and give you insights that job descriptions just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Google Cloud and Vertex AI. This is your chance to demonstrate your expertise and make a lasting impression on potential employers.
✨Tip Number 3
Prepare for technical interviews by brushing up on your ML concepts and coding skills. Practice common interview questions and scenarios related to MLOps. We all know that confidence is key, so get ready to shine!
✨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 and eager to join our team.
We think you need these skills to ace Senior ML Engineer – MLOps Platform on Google Cloud
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with production ML systems and Google Cloud. 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 MLOps and how your background makes you the perfect fit for our team. Let us know what excites you about this opportunity!
Showcase Your Technical Skills: We’re looking for someone with a deep understanding of Vertex AI and ML tooling. Be sure to mention specific tools and frameworks you’ve worked with, and any automation processes you’ve implemented in past roles.
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 Policy Expert
✨Know Your ML Stuff
Make sure you brush up on your machine learning concepts, especially around MLOps and Google Cloud. Be ready to discuss your past projects in detail, focusing on how you built production ML systems and automated processes.
✨Get Familiar with Vertex AI
Since this role emphasises a strong understanding of Vertex AI, dive deep into its features and functionalities. Prepare to explain how you've used it in previous roles or how you would approach using it for the company's needs.
✨Showcase Your Mentoring Skills
As you'll be mentoring team members, think of examples where you've successfully guided others. Be prepared to discuss your mentoring style and how you can help foster a collaborative environment.
✨Emphasise Reliability and Efficiency
This role requires a focus on reliability and efficiency in ML systems. Prepare to share specific strategies or frameworks you've implemented in the past that improved system performance and reliability.
