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- Tasks: Design and build end-to-end MLOps platforms for real-world machine learning applications.
- Company: Join a consultancy-led team delivering cutting-edge ML solutions.
- Benefits: Competitive day rate, remote work, and opportunities for travel.
- Why this job: Make a tangible impact by taking ML systems from experimentation to production.
- Qualifications: Strong MLOps experience and hands-on expertise with MLflow are essential.
- Other info: Dynamic role with a focus on collaboration and best practices in ML governance.
Contract Machine Learning Engineer | MLflow | Databricks | Production ML
Duration: Initially 3 months
Day rate: £500 - £550, Inside IR35
Workplace: Remote, with occasional travel to client-site
Inara are supporting a consultancy-led team delivering production-grade machine learning platforms for a range of end clients, and they’re looking for a senior, hands-on Contract MLOps Engineer to help take ML systems from experimentation into reliable, scalable production. This role is firmly focused on ML enablement and platform engineering rather than model research. You’ll be the person ensuring models can be trained, tracked, deployed, governed, and monitored properly in real-world environments.
What you’ll be doing:
- Designing and building end-to-end MLOps platforms that support the full ML lifecycle
- Implementing and operating MLflow for experiment tracking, model registry, and versioning
- Enabling production deployments of ML models (batch and/or real-time)
- Putting robust CI/CD pipelines in place for ML workflows
- Partnering closely with Data Scientists to move models from notebooks into production
- Establishing best practices around model governance, monitoring, retraining, and environments
- Integrating ML platforms with Databricks and cloud-native services
What we’re looking for:
- Strong, real-world MLOps experience (this is not a theoretical role)
- Deep hands-on MLflow experience — this is essential
- Proven track record of productionising ML models across multiple client or project environments
- Background in one or more of:
- MLOps / ML Engineering
- DevOps with ML platforms
- Data Science with a strong production focus
- MLflow (expert-level)
- Databricks
- Cloud platforms (AWS preferred; SageMaker exposure a bonus)
- CI/CD for ML workloads
- Docker and Kubernetes
- Infrastructure as Code (Terraform or similar)
- Python-based ML workflows
Machine Learning Engineer in Reading employer: Inara
Contact Detail:
Inara Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in Reading
✨Tip Number 1
Network like a pro! Reach out to your connections in the MLOps space and let them know you're on the hunt for a role. Attend meetups or webinars related to machine learning and engage with others in the field. You never know who might have a lead on that perfect contract!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your MLOps projects, especially those involving MLflow and Databricks. This will give potential employers a clear view of what you can bring to the table and how you can help them scale their ML systems.
✨Tip Number 3
Prepare for interviews by brushing up on real-world scenarios. Be ready to discuss how you've tackled challenges in productionising ML models and implementing CI/CD pipelines. Use specific examples from your experience to demonstrate your expertise and problem-solving skills.
✨Tip Number 4
Don't forget to apply through our website! We’ve got loads of opportunities that match your skill set. Plus, applying directly can sometimes give you an edge over other candidates. So, get your application in and let’s get you that dream role!
We think you need these skills to ace Machine Learning Engineer in Reading
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your hands-on experience with MLflow and any projects where you've taken models from experimentation to production. We want to see how your skills match what we're looking for!
Showcase Your Projects: Include specific examples of MLOps platforms you've designed or worked on. If you've implemented CI/CD pipelines or integrated ML platforms with Databricks, let us know! Real-world examples will make your application stand out.
Keep It Clear and Concise: When writing your application, be clear and concise. Use bullet points to break down your experience and skills. We appreciate straightforward communication, so make it easy for us to see why you're a great fit!
Apply Through Our Website: We encourage you to apply through our website for the best chance of getting noticed. It helps us keep track of applications and ensures you’re considered for the role. Don’t miss out on this opportunity!
How to prepare for a job interview at Inara
✨Know Your MLOps Inside Out
Make sure you brush up on your MLOps knowledge, especially around MLflow and Databricks. Be ready to discuss your hands-on experience with these tools and how you've implemented them in real-world scenarios.
✨Showcase Your Production Experience
Prepare specific examples of how you've taken machine learning models from experimentation to production. Highlight any challenges you faced and how you overcame them, as this will demonstrate your problem-solving skills.
✨Understand CI/CD Pipelines
Familiarise yourself with CI/CD processes for ML workflows. Be prepared to explain how you've set up robust pipelines in the past and the impact they had on the efficiency of model deployment.
✨Collaborate Like a Pro
Since you'll be partnering closely with Data Scientists, think about how you've worked in cross-functional teams before. Be ready to share examples of how you facilitated communication and collaboration to ensure smooth transitions from notebooks to production.