At a Glance
- Tasks: Build and deploy machine learning solutions on Azure and Databricks.
- Company: Innovative tech firm in Nottingham with a hybrid work culture.
- Benefits: Competitive pay, flexible working, and opportunities for professional growth.
- Other info: Exciting projects with potential for career advancement.
- Why this job: Join a dynamic team and make an impact in the ML field.
- Qualifications: Experience in ML engineering, Azure, Python, and CI/CD practices.
The predicted salary is between 60000 - 80000 £ per year.
Machine Learning / MLOps Engineer
Location
Nottingham, UK (Hybrid)
Employment Type
6-Month Fixed-Term Contract / Contract Inside IR35
Start Date
Immediate
We are seeking a Machine Learning / MLOps Engineer to help build, deploy, and support production-ready machine learning solutions on Azure and Databricks.
Working closely with Data Scientists, Data Engineers, Platform Engineers, and business stakeholders, you will be responsible for operationalising ML models, building scalable data and ML pipelines, implementing monitoring, and supporting the end-to-end ML lifecycle.
This role will initially span MLOps, data engineering, and platform activities while the capability continues to mature.
Key Responsibilities
- Deploy and operationalise machine learning models developed by Data Science teams.
- Build and maintain ML and data pipelines using Python, Py Spark, SQL, Azure, and Databricks.
- Develop and manage Databricks Workflows, Jobs, MLflow, and model deployment processes.
- Implement CI/CD pipelines and Git-based development practices.
- Build monitoring and ing for model performance, data quality, workflow failures, and operational health.
- Manage model lifecycle activities including versioning, deployment, testing, and continuous improvement.
- Collaborate with platform, cloud, Dev Ops, security, and operational teams to ensure scalable and secure deployments.
- Create deployment documentation, runbooks, and support processes.
Essential Skills & Experience
- Hands‑on experience as an ML Engineer, MLOps Engineer, or similar role.
• Strong experience with
- Azure Cloud
- Databricks
- Python, Py Spark, SQL
- MLflow and Databricks Workflows
- CI/CD and Git
- Machine Learning deployment and operational support.
- Experience building and maintaining production‑grade ML pipelines.
- Understanding of model monitoring, observability, testing, and governance.
- Experience working across Data Science, Engineering, and Platform teams.
- Strong troubleshooting, communication, and stakeholder management skills.
Desirable Skills
- Generative AI / LLM development experience (Lang Chain, Lang Graph, RAG frameworks).
- Unity Catalog and Databricks Model Registry.
- Azure Dev Ops, Git Hub Actions.
- Docker, Kubernetes (AKS), Azure Container Apps.
- Terraform or Infrastructure‑as‑Code tools.
- Retail, forecasting, recommendation, or personalisation use cases.
- Azure or Databricks certifications.
- #J-18808-Ljbffr
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning / MLOps Engineer_Nottingham (ML Engineer II)
✨Tap into Online Data Science Communities
Join online communities focused on data science like Kaggle, LinkedIn groups, or Reddit threads. These are goldmines for temporary gigs, as you can network with professionals and potentially hear about opportunities at companies like UST before they're even advertised!
✨Show Off Your Skills With Projects
Got some cool data science projects? Showcase them on platforms like GitHub or create a personal portfolio website. This visibility is crucial for landing temporary roles—let recruiters see your actual skills in action, which can set you apart from the crowd.
✨Check Out Specialist Job Boards
For temp roles, hit up job boards dedicated to tech and data science, like Stack Overflow Jobs or DataJobs. These platforms often feature openings that you won’t find on general job sites, including contracts with companies like UST.
✨Leverage University Resources
If you're currently at uni or recently graduated, tap into your school's career services. They often have connections with companies looking for temporary data science interns or contract workers, and they might even host job fairs with employers like UST.
We think you need these skills to ace Machine Learning / MLOps Engineer_Nottingham (ML Engineer II)
Some tips for your application 🫡
Highlight Your Data Projects:When applying for a temporary data science role at UST, make sure to showcase any relevant projects you've worked on. Whether it's a personal project, an academic undertaking, or contributions to an open-source initiative, detailing these experiences can really set you apart and demonstrate your practical skills.
Emphasise Your Analytical Skills:In your CV and cover letter, focus on the specific analytical skills that are key to data science. Mention any experience with statistical tools, programming languages like Python or R, and data visualisation software. Don't forget to include any certifications that may bolster your expertise!
Show Your Flexibility:Since this is a temporary role, it's important to convey your adaptability and willingness to learn. In your cover letter to UST, emphasise how quickly you can get up to speed with new tools or projects. Highlight any previous experiences where you've had to adjust to new environments or challenges.
Craft a Unique Data-Driven Cover Letter:Instead of the usual generic cover letter, spice it up with some data! Maybe you’ve improved a process by 20% in a past role or cleaned a dataset with over a million entries. Use these stats to your advantage to grab UST’s attention and show the tangible impact of your work.
How to prepare for a job interview at UST
✨Showcase Your Analytical Skills
For a data science gig, it's crucial to demonstrate your analytical abilities. Be ready to discuss previous projects and the methodologies you used. Think about how you can quantify your impact—did your analysis improve efficiency or save costs? These are the stories that will stick with interviewers at UST.
✨Brush Up on Technical Skills
You might face technical questions on tools relevant to data science, like Python, R, or SQL. Prepare to solve a problem live—perhaps they'll ask you to write a simple query or code snippet. It’s cool to talk about them, but we need to show we can do it in practice, especially in a temporary role where quick results matter.
✨Highlight Your Adaptability
Since this is a temporary position, emphasise your ability to learn quickly and adapt to new tools or workflows. Share examples of how you've thrived in fast-paced environments before, and how you can hit the ground running at UST.
✨Prepare a Portfolio of Your Work
Bring your portfolio to the table—showcase projects where you've leveraged data science techniques to solve problems. Whether it’s a GitHub repository or a set of case studies, having tangible examples of your work will help you stand out and show what you bring to the team at UST.
