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- Tasks: Design scalable ML pipelines and automate workflows in a hybrid role.
- Company: Leading tech firm in the UK with a focus on innovation.
- Benefits: Flexible working, 2 days in the office, and a dynamic team environment.
- Why this job: Join a cutting-edge team and make an impact in machine learning.
- Qualifications: Strong Python skills and experience with ML libraries, Docker, and AWS.
- Other info: Proactive collaboration and excellent career growth opportunities.
The predicted salary is between 36000 - 60000 £ per year.
A leading technology firm in the UK is looking for an Associate Delivery Consultant to support machine learning products in a hybrid role based in London.
Responsibilities include:
- Designing scalable ML pipelines
- Automating ML workflows
The ideal candidate should have:
- Strong Python skills
- Experience with ML libraries
- Experience with Docker
- Experience with cloud platforms like AWS
This contract position offers flexibility with 2 days per week in the office and seeks someone proactive in collaboration and deployment of ML systems.
ML Ops Engineer: Production ML Pipelines & Automation in England employer: Stott and May
Contact Detail:
Stott and May Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Ops Engineer: Production ML Pipelines & Automation in England
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with ML professionals on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those involving Python, Docker, and cloud platforms. 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 common ML concepts and tools. Be ready to discuss your experience with scalable ML pipelines and automation. Practising mock interviews can help you feel more confident when it’s time to shine.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that might be perfect for you. Plus, applying directly can sometimes give you an edge over other candidates.
We think you need these skills to ace ML Ops Engineer: Production ML Pipelines & Automation in England
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your Python skills and experience with ML libraries, Docker, and cloud platforms like AWS. We want to see how your background aligns with the role of an ML Ops Engineer!
Craft a Compelling Cover Letter: Use your cover letter to showcase your passion for machine learning and automation. Tell us why you're excited about this role and how you can contribute to our team at StudySmarter.
Showcase Your Projects: If you've worked on any relevant projects, whether personal or professional, make sure to mention them! We love seeing practical examples of your skills in action, especially in designing scalable ML pipelines.
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 Stott and May
✨Know Your ML Pipelines
Make sure you understand the fundamentals of machine learning pipelines. Be ready to discuss how you've designed and implemented scalable ML workflows in the past. Highlight any specific projects where you used Python, Docker, or cloud platforms like AWS.
✨Show Off Your Collaboration Skills
Since the role requires proactive collaboration, prepare examples of how you've worked effectively in teams. Think about times when you contributed to a project’s success through teamwork, especially in deploying ML systems.
✨Brush Up on Automation Techniques
Familiarise yourself with automation tools and techniques relevant to ML workflows. Be prepared to explain how you've automated processes in previous roles, and discuss the impact it had on efficiency and productivity.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's approach to ML and their current projects. This shows your genuine interest in the role and helps you gauge if the company is the right fit for you.
