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- Tasks: Design and maintain MLOps environments for seamless model deployment and monitoring.
- Company: Dynamic tech firm focused on innovative machine learning solutions.
- Benefits: Competitive pay, flexible working arrangements, and opportunities for skill enhancement.
- Why this job: Join a cutting-edge team and shape the future of machine learning in real-world applications.
- Qualifications: Strong MLOps experience with hands-on MLflow expertise is essential.
- Other info: Exciting role with potential for growth in a fast-paced environment.
The predicted salary is between 50000 - 70000 £ per year.
We are seeking an experienced MLOps Engineer with a strong background in DevOps, Data Science, or Machine Learning Engineering, who has hands-on experience productionising ML models. The focus of the role is building and enabling production-grade ML environments rather than model development itself. Candidates must have deep MLflow experience and proven delivery in real-world client settings.
Key Responsibilities
- Design, build, and maintain end-to-end MLOps environments to support model training, tracking, deployment, and monitoring
- Implement MLflow for: Experiment tracking; Model registry; Model versioning and lifecycle management
- Enable model deployment into production (batch and/or real-time) with robust CI/CD
- Work closely with Data Scientists to transition models from experimentation to production
- Build scalable, secure, and reproducible ML platforms
- Establish best practices around: Model governance; Monitoring and retraining; Environment management
- Integrate with cloud and data platforms such as Databricks, and potentially AWS SageMaker
Essential Experience
- Strong MLOps background, not just theoretical knowledge
- Extensive hands-on MLflow experience (non-negotiable)
- Demonstrable experience productionising ML models for at least 2–3 client engagements
- Background in one or more of: DevOps; Data Science / Machine Learning Engineering; Data Engineering (not required, but acceptable if MLOps-led)
- Experience designing and supporting ML platforms in production environments
Technical Skills (Required / Highly Desirable)
- MLflow
- Databricks
- Cloud platforms (AWS preferred; SageMaker experience a plus)
- CI/CD for ML (e.g. GitHub Actions, GitLab CI, Azure DevOps, etc.)
- Containerisation and orchestration (Docker, Kubernetes)
- Infrastructure as Code (Terraform or similar)
- Python-centric ML workflows
Sponsorship not available for this role.
MLOps Engineer in London employer: Kleboe Jardine Ltd
Contact Detail:
Kleboe Jardine Ltd 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 your connections in the MLOps space, attend meetups, and engage in online forums. 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 MLflow and productionising models. This will give potential employers a clear view of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and real-world experiences. Be ready to discuss how you've implemented CI/CD pipelines or worked with cloud platforms like AWS or Databricks.
✨Tip Number 4
Don't forget to apply through our website! We’ve got loads of opportunities that might just be the perfect fit for you. Plus, it’s a great way to ensure your application gets seen by the right people.
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 MLOps experience, especially with MLflow. We want to see how you've productionised ML models in real-world settings, so don’t hold back on those details!
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. We love seeing enthusiasm and a clear understanding of the responsibilities, so let your personality come through.
Showcase Relevant Projects: If you've worked on any projects that involved building MLOps environments or using CI/CD for ML, make sure to mention them. We’re keen to see your hands-on experience and how you’ve tackled challenges in past roles.
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 Kleboe Jardine Ltd
✨Know Your MLflow Inside Out
Since deep MLflow experience is a must-have for this role, make sure you can discuss its features and functionalities confidently. Prepare to share specific examples of how you've used MLflow for experiment tracking, model registry, and lifecycle management in your previous projects.
✨Showcase Your Production Experience
Be ready to talk about your hands-on experience in productionising ML models. Highlight at least 2-3 client engagements where you successfully transitioned models from experimentation to production, focusing on the challenges you faced and how you overcame them.
✨Familiarise Yourself with CI/CD Practices
As the role involves implementing robust CI/CD pipelines, brush up on your knowledge of tools like GitHub Actions or Azure DevOps. Be prepared to discuss how you've integrated these tools into your MLOps workflows to ensure smooth deployments.
✨Understand Cloud Integration
Since the job mentions integration with cloud platforms like Databricks and AWS SageMaker, do your homework on these technologies. Be ready to explain how you've leveraged cloud services in your past projects to build scalable and secure ML environments.