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- Tasks: Architect AI deployment infrastructure and enhance data science platforms.
- Company: Leading tech faculty in London with a focus on innovation.
- Benefits: Hybrid working, unlimited annual leave, and competitive perks.
- Other info: Exciting opportunities for growth in a collaborative environment.
- Why this job: Join a dynamic team and shape the future of machine learning.
- Qualifications: Proficiency in Python, Go, Docker, Kubernetes, and MLOps tools.
The predicted salary is between 60000 - 80000 £ per year.
Faculty seeks a Software Engineer to architect infrastructure for AI deployment in London. This role involves ownership of MLOps tooling and collaboration with technologists to enhance data science platforms.
Candidates must have skills in Python, Go, Docker, and Kubernetes, with a passion for internal tools and a strong understanding of the machine learning lifecycle.
The position offers hybrid working and an unlimited annual leave policy, among other benefits.
Platform Engineer: ML Infra & MLOps Architect in London employer: Faculty
Contact Detail:
Faculty Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Platform Engineer: ML Infra & MLOps Architect in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Python, Go, Docker, and Kubernetes. This gives us a tangible way to see what you can do.
✨Tip Number 3
Prepare for the tech interview! Brush up on your knowledge of MLOps and the machine learning lifecycle. We love candidates who can talk about their experiences and how they’ve tackled challenges.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we’re always on the lookout for passionate individuals ready to make an impact.
We think you need these skills to ace Platform Engineer: ML Infra & MLOps Architect in London
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with Python, Go, Docker, and Kubernetes in your application. We want to see how you've used these tools in real-world scenarios, so don’t hold back!
Talk About MLOps: Since this role is all about MLOps tooling, share any relevant projects or experiences you have in this area. We love seeing candidates who are passionate about the machine learning lifecycle and can demonstrate their understanding.
Be Yourself: We’re looking for genuine passion and enthusiasm, so let your personality shine through in your application. Don’t be afraid to show us what makes you tick and why you’re excited about this role!
Apply Through Our Website: To make sure your application gets the attention it deserves, apply directly through our website. It’s the best way for us to connect with you and keep track of your application!
How to prepare for a job interview at Faculty
✨Know Your Tech Stack
Make sure you’re well-versed in Python, Go, Docker, and Kubernetes. Brush up on your knowledge of these technologies and be ready to discuss how you've used them in past projects. This will show that you’re not just familiar with the tools but can also apply them effectively.
✨Understand MLOps Inside Out
Since this role focuses on MLOps tooling, dive deep into the machine learning lifecycle. Be prepared to talk about your experience with deploying AI models and how you’ve optimised workflows. Showing a solid grasp of MLOps principles will set you apart from other candidates.
✨Show Your Passion for Internal Tools
This position values a passion for internal tools, so think of examples where you’ve improved or built tools that enhance productivity. Share specific instances where your contributions made a difference, as this will demonstrate your commitment to enhancing data science platforms.
✨Prepare for Collaboration Questions
Collaboration is key in this role, so expect questions about teamwork and communication. Think of scenarios where you’ve worked closely with technologists or cross-functional teams. Highlight your ability to collaborate effectively and how it led to successful outcomes.