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- Tasks: Deploy and monitor machine learning models in production environments.
- Company: Join a forward-thinking tech company focused on ML innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for growth.
- Why this job: Be at the forefront of ML technology and make a real impact.
- Qualifications: Strong Python skills and experience with ML model deployment.
- Other info: Dynamic team environment with excellent career advancement potential.
The predicted salary is between 36000 - 60000 £ per year.
We are seeking a highly skilled MLOps Engineer to focus on the deployment, monitoring, and maintenance of machine learning models in production environments. This role is platform-focused and does not involve model development or end-user support. The successful candidate will ensure reliability, scalability, and performance of ML platforms while managing API endpoints and deployment workflows.
Key Responsibilities
- Platform Operations & Monitoring
- Monitor ML model endpoints and platform health using tools such as Grafana and Domino Data Lab
- Respond to incidents and alerts; perform code fixes and manage changes via ServiceNow
- Liaise with Domino Data Lab support to resolve platform-related issues
- Model Deployment
- Deploy and maintain ML models in production environments
- Ensure models integrate seamlessly into automated pipelines
- Maintain reliability, version control, and governance standards
- Pipeline Maintenance
- Collaborate with Data Scientists and Engineers for smooth production handoff
- Maintain and optimize ML pipelines for stability and scalability
- Improve performance, resource usage, and automation
- Automation & Tooling
- Implement automation for deployment and monitoring
- Contribute to continuous platform improvements
Required Skills & Experience
- Strong Python programming experience
- Proven experience deploying and monitoring ML models in production
- Understanding of model evaluation metrics, data drift, overfitting, and feature importance
- Experience with AWS services (S3, Redshift, etc.)
- Hands-on experience with Grafana for monitoring
- Familiarity with Domino Data Lab (desirable)
- Strong knowledge of CI/CD, version control, Docker, Kubernetes
- Excellent troubleshooting and incident management skills
- Strong stakeholder communication skills
MLOps Engineer in London employer: ALOIS Solutions
Contact Detail:
ALOIS Solutions Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land MLOps Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the MLOps community on LinkedIn or attend meetups. We can’t stress enough how personal connections can lead to job opportunities.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your MLOps projects, especially those involving deployment and monitoring. We love seeing practical examples of your work!
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and AWS knowledge. We recommend practicing common MLOps scenarios and troubleshooting exercises to impress your interviewers.
✨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 looking for passionate candidates like you!
We think you need these skills to ace MLOps Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with deploying and monitoring ML models. We want to see how your skills align with the role, so don’t be shy about showcasing your Python programming and AWS experience!
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 your background makes you a perfect fit for our team. We love seeing enthusiasm and a bit of personality!
Showcase Relevant Projects: If you've worked on any projects related to ML model deployment or monitoring, make sure to mention them. We’re keen to see real-world examples of your work, especially if they involved tools like Grafana or Domino Data Lab.
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 – just a few clicks and you’re done!
How to prepare for a job interview at ALOIS Solutions
✨Know Your Tools Inside Out
Make sure you’re familiar with the tools mentioned in the job description, like Grafana and Domino Data Lab. Brush up on how to monitor ML model endpoints and platform health, as this will likely come up during your interview.
✨Showcase Your Python Skills
Since strong Python programming experience is a must, be prepared to discuss your past projects or experiences where you used Python for deploying and monitoring ML models. Maybe even have a couple of code snippets ready to demonstrate your skills!
✨Understand the Deployment Process
Be ready to talk about the deployment workflows you’ve managed in the past. Highlight your experience with CI/CD, version control, Docker, and Kubernetes, as these are crucial for the role. Sharing specific examples will help you stand out.
✨Communicate Effectively
Strong stakeholder communication skills are essential. Practice explaining complex technical concepts in simple terms, as you may need to liaise with non-technical team members. Good communication can make a big difference in how you’re perceived during the interview.