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- Tasks: Design and operate data pipelines for machine learning systems.
- Company: Join a forward-thinking tech company focused on ML innovation.
- Benefits: Attractive salary, flexible working options, and growth opportunities.
- Why this job: Be at the forefront of ML technology and make a real impact.
- Qualifications: Experience in data engineering and ML infrastructure is essential.
- Other info: Dynamic team environment with plenty of chances to learn and grow.
The predicted salary is between 60000 - 80000 £ per year.
We are hiring an ML Ops Engineer / Data Engineer to own the reliability, scalability, and operational integrity of our machine-learning systems in research and production. This role sits at the intersection of data engineering and ML infrastructure: you'll design and operate data pipelines that feed models, and you'll build the tooling that trains, deploys, monitors, and retrains them.
You'll work closely with research engineers and product teams, taking models from experimentation to production-grade systems with clear SLAs, reproducibility guarantees, and observable behaviour. This is not a research role; it is a hands-on engineering role focused on making ML systems work reliably at scale.
What You'll Work On:
- ML lifecycle infrastructure
- Productionizing models: packaging, deployment, versioning, and rollback
- Designing CI/CD pipelines for ML (training → validation → deployment)
- Implementing model monitoring (data drift, prediction drift, performance decay)
- Managing experiment tracking
ML Ops / Data Engineer in London employer: CMC Markets UK Plc
Contact Detail:
CMC Markets UK Plc Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Ops / Data Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the ML Ops and Data Engineering space on LinkedIn or at meetups. We can’t stress enough how valuable personal connections can be when it comes to landing that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving data pipelines and ML systems. We love seeing practical examples of what you can do, so make sure to highlight your hands-on experience.
✨Tip Number 3
Prepare for technical interviews by brushing up on your knowledge of CI/CD pipelines and model monitoring. We recommend doing mock interviews with friends or using online platforms to get comfortable with the types of questions you might face.
✨Tip Number 4
Don’t forget to 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 candidates who are ready to dive into the world of ML Ops and Data Engineering.
We think you need these skills to ace ML Ops / Data Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in ML Ops and data engineering. We want to see how your skills align with the role, so don’t be shy about showcasing your projects and achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about ML Ops and how you can contribute to our team. Keep it concise but engaging – we love a good story!
Showcase Your Technical Skills: Don’t forget to mention the tools and technologies you’re familiar with, especially those related to CI/CD pipelines and model monitoring. We’re keen on seeing how you can help us build reliable ML systems!
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 the role. Plus, it’s super easy!
How to prepare for a job interview at CMC Markets UK Plc
✨Know Your ML Ops Fundamentals
Make sure you brush up on the core concepts of ML Ops and data engineering. Understand the lifecycle of machine learning models, from training to deployment. Be ready to discuss how you would handle versioning and rollback in production.
✨Showcase Your Hands-On Experience
Prepare to share specific examples of projects where you've designed and operated data pipelines or built tooling for ML systems. Highlight your experience with CI/CD pipelines and any challenges you faced while productionising models.
✨Demonstrate Problem-Solving Skills
Expect scenario-based questions that test your ability to troubleshoot issues in ML systems. Think about how you would monitor for data drift or performance decay and be ready to explain your thought process clearly.
✨Collaborate and Communicate
Since this role involves working closely with research engineers and product teams, be prepared to discuss how you approach collaboration. Share examples of how you've effectively communicated technical concepts to non-technical stakeholders.