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- Tasks: Build and deploy scalable ML systems while collaborating with Data Scientists.
- Company: Leading tech business in Greater London with a focus on innovation.
- Benefits: Dynamic work environment, rapid experimentation, and safe AI deployment.
- Why this job: Join a team that enhances digital experiences through cutting-edge machine learning solutions.
- Qualifications: 3-5 years of experience, strong AWS skills, and MLOps knowledge.
The predicted salary is between 48000 - 72000 £ per year.
A leading technology business in Greater London is seeking a Senior Machine Learning Engineer to build and deploy scalable ML systems. This role involves collaborating with Data Scientists to enhance digital experiences through innovative applied machine learning solutions.
The ideal candidate has 3-5 years of experience, strong cloud skills, especially with AWS, and is familiar with MLOps best practices. The position offers a dynamic work environment with a focus on rapid experimentation and safe AI deployment.
Senior ML Engineer — Production AI, MLOps & Cloud in London employer: Xcede
Contact Detail:
Xcede Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior ML Engineer — Production AI, MLOps & Cloud in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the tech industry, especially those working with ML and cloud technologies. A friendly chat can lead to insider info about job openings that aren't even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those involving AWS and MLOps. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice common ML scenarios and be ready to discuss how you've tackled challenges in past projects.
✨Tip Number 4
Don't forget to apply through our website! We make it easy for you to find the right role and connect with us directly. Plus, it shows you're genuinely interested in joining our team!
We think you need these skills to ace Senior ML Engineer — Production AI, MLOps & Cloud in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in building and deploying scalable ML systems. We want to see how your skills align with the role, especially your cloud expertise and familiarity with MLOps best practices.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about applied machine learning. Share specific examples of projects you've worked on that demonstrate your ability to enhance digital experiences through innovative solutions.
Showcase Your Collaboration Skills: Since this role involves working closely with Data Scientists, highlight any past experiences where you collaborated effectively. We love seeing teamwork in action, so don’t hold back on those stories!
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’re considered for this exciting opportunity in our dynamic work environment.
How to prepare for a job interview at Xcede
✨Know Your ML Fundamentals
Brush up on your machine learning concepts and algorithms. Be ready to discuss how you've applied these in real-world scenarios, especially in building and deploying scalable ML systems.
✨Showcase Your Cloud Expertise
Since strong cloud skills, particularly with AWS, are crucial for this role, prepare to talk about your experience with cloud services. Have examples ready that demonstrate how you've leveraged AWS for ML projects.
✨Familiarise Yourself with MLOps
Understand MLOps best practices and be prepared to discuss how you’ve implemented them in previous roles. This could include topics like CI/CD pipelines for ML models or monitoring model performance post-deployment.
✨Emphasise Collaboration Skills
This role involves working closely with Data Scientists, so highlight your teamwork experiences. Share specific examples of how you've collaborated on projects to enhance digital experiences through applied machine learning solutions.
