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- Tasks: Design and maintain MLOps environments for deploying and monitoring ML models.
- Company: Dynamic tech firm focused on innovative machine learning solutions.
- Benefits: Competitive pay, flexible working arrangements, and opportunities for skill development.
- 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 and proven client delivery.
- Other info: Exciting role with potential for growth in a fast-paced environment.
The predicted salary is between 50000 - 70000 £ per year.
Inside IR35 contract | 3-6 month 1-2 days onsite requirement London or Birmingham
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 employer: Kleboe Jardine Ltd
Contact Detail:
Kleboe Jardine Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land MLOps Engineer
✨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. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and real-world applications. Be ready to discuss how you've implemented CI/CD pipelines or managed model lifecycles in previous roles.
✨Tip Number 4
Don't forget to apply through our website! We make it easy for you to find roles that match your skills and experience. Plus, it shows you're serious about joining our team!
We think you need these skills to ace MLOps Engineer
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! Tell us why you're passionate about MLOps and how your background in DevOps or Data Science makes you the perfect fit for this role. Keep it engaging and relevant!
Showcase Your Technical Skills: We’re looking for hands-on experience, so be sure to mention your familiarity with tools like Databricks, CI/CD processes, and containerisation. Highlight specific projects where you’ve used these skills to make an impact.
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your details and get the ball rolling on your application. We can’t wait to hear from you!
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.
✨Demonstrate Your CI/CD Knowledge
Familiarise yourself with CI/CD practices for ML, especially tools like GitHub Actions or Azure DevOps. Be prepared to explain how you've implemented robust CI/CD pipelines in your past roles, and how they contributed to smoother deployments and better model governance.
✨Connect with Data Scientists
Since collaboration with Data Scientists is key, think of examples where you've worked closely with them to enable model deployment. Discuss how you facilitated the transition from experimentation to production, and any best practices you established around monitoring and retraining.