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- Tasks: Ensure smooth operation of ML models and improve platform reliability.
- Company: Dynamic tech company in London with a hybrid work model.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Why this job: Join a cutting-edge team and make an impact in the world of machine learning.
- Qualifications: Strong Python skills and experience in ML deployment and monitoring.
- Other info: Collaborative environment with a focus on innovation and efficiency.
The predicted salary is between 54000 - 84000 £ per year.
We are looking for an experienced MLOps Engineer to support the deployment, monitoring, and ongoing maintenance of machine learning models in production environments. This role is focused purely on platform reliability, deployment, and operational excellence — not model development or end-user support.
- Ensuring production ML systems run smoothly
- Managing deployment workflows
- Monitoring model performance
- Maintaining scalable and reliable ML infrastructure
- Supporting API endpoints and platform integrations
Key Responsibilities
- Platform Operations & Monitoring
- Monitor ML model endpoints and platform health using tools such as Grafana
- Respond to alerts, troubleshoot incidents, and implement code fixes
- Manage incidents and change requests through internal processes
- Coordinate with platform vendors/support teams to resolve technical issues
- Model Deployment
- Deploy and maintain ML models in production environments
- Ensure models are integrated into automated pipelines
- Maintain high standards of reliability, performance, and scalability
- Pipeline Maintenance
- Collaborate with Data Scientists and Engineering teams to transition models into production
- Maintain and support ML pipelines
- Optimize pipeline performance, automation, and resource utilization
- Automation & Platform Improvement
- Implement automation for deployment and monitoring
- Improve platform efficiency and operational workflows
- Support scalable infrastructure and production readiness
Required Skills & Experience
- Strong hands‑on experience in Python
- Proven experience in ML model deployment & production monitoring
- Good understanding of core Data Science concepts such as:
- Model evaluation metrics
- Overfitting
- Data drift
- Feature importance
- CI/CD pipelines
- Version control (Git)
- Containerization (Docker)
- Orchestration (Kubernetes preferred)
Nice to Have
- Experience working with enterprise ML platforms (e.g., Domino or similar)
Senior Machine Learning Engineer employer: ALOIS UK
Contact Detail:
ALOIS UK Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with MLOps 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 MLOps projects, especially those involving deployment and monitoring. 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 skills and understanding of CI/CD pipelines. Practice common troubleshooting scenarios and be ready to discuss how you've handled incidents in the past.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got some fantastic opportunities waiting for you, and applying directly can sometimes give you a leg up in the process.
We think you need these skills to ace Senior Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with MLOps, Python, and any relevant tools like AWS or Grafana. We want to see how your skills match the role, so don’t be shy about showcasing your achievements!
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. Keep it engaging and personal – we love to see your 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 and how you’ve tackled challenges in production environments.
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 follow the prompts!
How to prepare for a job interview at ALOIS UK
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, especially Python, AWS services, and tools like Grafana. Brush up on your knowledge of CI/CD pipelines and containerization with Docker, as these are crucial for the role.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific incidents where you’ve troubleshot issues in ML model deployment or monitoring. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight your ability to manage incidents effectively.
✨Understand the Importance of Collaboration
Since this role involves working closely with Data Scientists and Engineering teams, be ready to talk about your experience collaborating across functions. Share examples of how you’ve successfully transitioned models into production and maintained pipelines.
✨Demonstrate Your Commitment to Operational Excellence
Emphasise your understanding of platform reliability and operational workflows. Discuss any past experiences where you improved efficiency or implemented automation in deployment and monitoring processes, showcasing your proactive approach to maintaining high standards.