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- Tasks: Manage and optimise machine learning pipelines while collaborating with data scientists.
- Company: Cutting-edge tech company based in Bristol with a focus on innovation.
- Benefits: Full-time role with professional development opportunities and a supportive environment.
- Why this job: Join a dynamic team and make an impact in the exciting field of machine learning.
- Qualifications: Experience in DevOps, strong analytical skills, and proficiency in Python.
- Other info: Great opportunity for career growth in a fast-paced tech environment.
The predicted salary is between 36000 - 60000 £ per year.
A cutting-edge technology company in Bristol is seeking an experienced MLOps Engineer to manage and optimize the machine learning lifecycle. You will work closely with data scientists and engineers to ensure seamless integration and reliability of machine learning models.
Ideal candidates will have experience in DevOps, a strong analytical mindset, and expertise in programming with Python and other languages. This full-time role offers a supportive environment with opportunities for professional development.
MLOps Engineer: Build Scalable ML Pipelines in Bristol employer: Mobizy
Contact Detail:
Mobizy Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land MLOps Engineer: Build Scalable ML Pipelines in Bristol
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at local meetups. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your MLOps projects, especially those involving scalable ML pipelines. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and DevOps knowledge. Practice common interview questions and coding challenges to boost your confidence and impress the hiring team.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities waiting for talented MLOps Engineers like you. Plus, it’s a great way to get noticed by our hiring team directly.
We think you need these skills to ace MLOps Engineer: Build Scalable ML Pipelines in Bristol
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in MLOps and DevOps. We want to see how your skills align with the job description, so don’t be shy about showcasing your Python expertise and any relevant projects you've worked on.
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 you can contribute to our team. We love seeing candidates who can connect their experiences to our mission.
Showcase Your Analytical Skills: Since we’re looking for someone with a strong analytical mindset, make sure to include examples of how you've solved complex problems in your previous roles. We want to know how you think and approach challenges!
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. Plus, it shows you’re keen to join us!
How to prepare for a job interview at Mobizy
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, especially Python and any other programming languages relevant to MLOps. Brush up on your knowledge of DevOps practices and be ready to discuss how you've applied them in previous roles.
✨Showcase Your Analytical Skills
Prepare examples that highlight your analytical mindset. Think of specific projects where you optimised machine learning models or improved pipelines. Be ready to explain your thought process and the impact of your work on the overall project.
✨Collaboration is Key
Since you'll be working closely with data scientists and engineers, be prepared to discuss your experience in collaborative environments. Share instances where teamwork led to successful outcomes, and emphasise your communication skills.
✨Ask Insightful Questions
At the end of the interview, don’t forget to ask questions! Inquire about the company’s approach to machine learning lifecycle management or how they support professional development. This shows your genuine interest in the role and helps you gauge if it’s the right fit for you.
