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- Tasks: Design and build MLOps platforms for real-world machine learning applications.
- Company: Join a consultancy-led team focused on innovative ML solutions.
- Benefits: Competitive day rate, remote work, and opportunities for travel.
- Why this job: Make a tangible impact by deploying cutting-edge ML models in production.
- Qualifications: Strong MLOps experience and hands-on expertise with MLflow required.
- Other info: Dynamic role with potential for career advancement in the tech industry.
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 Cambridge employer: Inara
Contact Detail:
Inara Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in Cambridge
✨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 taste of what you can do and set you apart from the competition.
✨Tip Number 3
Prepare for interviews by brushing up on real-world scenarios. Be ready to discuss how you've taken models from experimentation to production, and share specific examples of CI/CD pipelines you've implemented. This hands-on experience is what they're really after!
✨Tip Number 4
Don't forget to apply through our website! We’ve got loads of opportunities that might just be the right fit for you. Plus, applying directly can sometimes give you an edge over other candidates. So, get your application in and let’s get you that dream job!
We think you need these skills to ace Machine Learning Engineer in Cambridge
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 applications that get to the point without unnecessary fluff!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. We can’t wait to see what you bring to the table!
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 specific projects where you've implemented these tools, as real-world experience is key for this role.
✨Showcase Your CI/CD Skills
Prepare to talk about how you've set up CI/CD pipelines for ML workflows in the past. Have examples ready that demonstrate your ability to automate and streamline the deployment process, as this will be crucial for the position.
✨Collaborate Like a Pro
Since you'll be partnering with Data Scientists, think of examples where you've successfully collaborated with cross-functional teams. Highlight how you’ve helped move models from notebooks into production and the impact it had on the project.
✨Be Ready for Technical Questions
Expect some deep technical questions related to cloud platforms, Docker, Kubernetes, and Infrastructure as Code. Brush up on these topics and be prepared to explain your thought process and decision-making in previous projects.