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- Tasks: Develop and maintain machine learning pipelines while collaborating with data scientists.
- Company: Leading financial services firm in the UK with a focus on innovation.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Exciting environment with a focus on performance and collaboration.
- Why this job: Join a dynamic team and make an impact in the world of finance through ML.
- Qualifications: Strong Python skills and experience in deploying ML models in production.
The predicted salary is between 60000 - 80000 £ per year.
A leading financial services firm in the UK is seeking an experienced ML Engineer to develop and maintain machine learning pipelines. You will collaborate closely with data scientists to productionise models and ensure their performance.
The ideal candidate has strong Python skills, experience deploying ML models in production, and understands CI/CD concepts.
This role offers a hybrid work model in a dynamic environment focused on innovation and performance.
Production ML Engineer: Pipelines & Real‑Time Deployments employer: Compare the Market
Contact Detail:
Compare the Market Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Production ML Engineer: Pipelines & Real‑Time Deployments
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those working in financial services or ML. 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 pipelines and real-time deployments. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and CI/CD knowledge. Practice coding challenges and be ready to discuss how you've deployed ML models in the past. Confidence is key!
✨Tip Number 4
Don't forget to apply through our website! We make it easy for you to find roles that match your skills and interests. Plus, it shows you're serious about joining our innovative team.
We think you need these skills to ace Production ML Engineer: Pipelines & Real‑Time Deployments
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your Python skills and experience with ML pipelines. We want to see how your background aligns 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 ML and how you can contribute to our innovative environment. Let us know what excites you about this role!
Showcase Your CI/CD Knowledge: Since we’re looking for someone who understands CI/CD concepts, make sure to mention any relevant experience you have. We love seeing practical examples of how you've deployed ML models in production!
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 Compare the Market
✨Know Your ML Pipelines
Make sure you can discuss your experience with machine learning pipelines in detail. Be ready to explain how you've developed and maintained them in previous roles, and share specific examples of challenges you faced and how you overcame them.
✨Showcase Your Python Skills
Since strong Python skills are a must for this role, brush up on your coding knowledge. Prepare to solve coding problems or discuss your past projects that involved Python, especially those related to ML model deployment.
✨Understand CI/CD Concepts
Familiarise yourself with Continuous Integration and Continuous Deployment (CI/CD) practices. Be prepared to discuss how you've implemented these concepts in your work, as well as the tools you've used to streamline the deployment process.
✨Emphasise Collaboration
This role involves working closely with data scientists, so highlight your teamwork skills. Think of examples where you've successfully collaborated with others to productionise models and ensure their performance, and be ready to discuss how you handle feedback and differing opinions.