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- Tasks: Build and maintain ML pipelines, ensuring smooth deployment and performance.
- Company: Dynamic tech firm in London focused on scaling machine learning capabilities.
- Benefits: Flexible hybrid work, competitive pay, and opportunities for professional growth.
- Why this job: Join a cutting-edge team and make a real impact in the ML space.
- Qualifications: Experience in MLOps, Python, Azure, and containerised environments.
- Other info: Exciting projects with potential for career advancement in a collaborative environment.
The predicted salary is between 48000 - 72000 £ per year.
Connecting UK projects with MLOps and Data Science contractors! Contract London based (hybrid or onsite depending on preference). I am supporting a client in London who is preparing to scale their machine learning capability. They need an experienced MLOps Engineer who can take models from development into a reliable production environment and keep everything running smoothly throughout the lifecycle.
What you will be working on:
- Building, improving and maintaining the end to end ML pipeline from data ingestion through to deployment.
- Working closely with Data Scientists to turn notebooks and prototypes into production ready workloads.
- Setting up CI and CD for model training, testing and deployment using Python based toolsets.
- Managing model performance, drift monitoring and regular retraining workflows.
- Creating infrastructure using Terraform or similar, ideally within Azure.
- Maintaining containerised workloads using Docker and Kubernetes.
- Supporting observability by putting in place clear logging, alerting and metrics for ML services.
- Ensuring best practice for version control, experiment tracking and model registry.
- Troubleshooting production issues and improving stability and performance across the ML platform.
Requirements:
- Strong Python for production environments.
- Solid experience with Azure for ML workloads including compute, networking and storage.
- Good knowledge of MLOps tools such as MLflow, Azure ML, Kubeflow or similar.
- Strong background in CI and CD using GitHub Actions, Azure DevOps or similar.
- Experience working in containerised environments using Docker and Kubernetes.
- Clear understanding of how to support Data Scientists and engineering teams through the model lifecycle.
Useful to have:
- Experience working on large scale data or ML projects.
- Good understanding of data engineering fundamentals.
- Experience working in regulated or high availability environments.
If you're an experienced MLOps Engineer, feel free to apply or send your CV to nokakpu@revoco-talent.co.uk.
Machine Learning Engineer employer: Revoco
Contact Detail:
Revoco Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to your connections in the MLOps and Data Science fields. Attend meetups, webinars, or even local tech events in London. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving ML pipelines, CI/CD processes, and containerisation. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on common MLOps scenarios. Be ready to discuss how you've tackled model performance issues or implemented CI/CD in past roles. Practice makes perfect, so consider mock interviews with friends or mentors.
✨Tip Number 4
Don't forget to apply through our website! We’ve got loads of opportunities that match your skills. Plus, it’s a great way to get noticed by recruiters who are looking for top talent like you!
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with MLOps, Python, and Azure, and don’t forget to mention any relevant projects you've worked on that align with the job description.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're the perfect fit for this role. Mention specific skills like CI/CD, Docker, and Kubernetes, and how they relate to the responsibilities outlined in the job description.
Showcase Your Projects: If you’ve worked on any notable ML projects, make sure to include them in your application. Describe your role, the technologies you used, and the impact of your work. This will help us see your practical experience in action!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at Revoco
✨Know Your Tech Stack
Make sure you’re well-versed in the specific tools and technologies mentioned in the job description. Brush up on your Python skills, Azure capabilities, and MLOps tools like MLflow or Kubeflow. Being able to discuss your hands-on experience with these will show that you’re ready to hit the ground running.
✨Showcase Your Projects
Prepare to talk about your previous projects, especially those involving end-to-end ML pipelines. Highlight how you’ve taken models from development to production, and be ready to discuss any challenges you faced and how you overcame them. Real-world examples will make your experience more relatable and impressive.
✨Understand the Lifecycle
Familiarise yourself with the entire machine learning lifecycle, from data ingestion to deployment and monitoring. Be prepared to discuss how you manage model performance and retraining workflows. This shows that you not only understand the technical aspects but also the importance of maintaining a reliable system.
✨Ask Insightful Questions
Interviews are a two-way street! Prepare thoughtful questions about the company’s current ML projects, their approach to CI/CD, or how they handle model drift. This demonstrates your genuine interest in the role and helps you assess if the company is the right fit for you.
