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Machine Learning / MLOps Engineer

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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, PySpark, 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 alerting 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, DevOps, security, and operational teams to ensure scalable and secure deployments.
- Create deployment documentation, runbooks, and support processes.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
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No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Essential Skills & Experience:
- Hands-on experience as an ML Engineer, MLOps Engineer, or similar role.
- Strong experience with:
- Azure Cloud
- Databricks
- Python, PySpark, 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.


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Desirable Skills:
- Generative AI / LLM development experience (LangChain, LangGraph, RAG frameworks).
- Unity Catalog and Databricks Model Registry.
- Azure DevOps, GitHub Actions.
- Docker, Kubernetes (AKS), Azure Container Apps.
- Terraform or Infrastructure-as-Code tools.
- Retail, forecasting, recommendation, or personalisation use cases.
- Azure or Databricks certifications.
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