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# Machine Learning / MLOps Engineer_Nottingham (ML Engineer II)

- **Company:** UST
- **Location:** Nottingham, UK
- **Contract:** Temporary contract
- **Skills:** Microsoft Azure, Cloud Computing, Continuous Integration, Information Engineering, DevOps, Github, Monitoring of Systems, Python (Programming Language), Machine Learning, Runbook, SQL Databases, Management of Software Versions, Large Language Models, Software Troubleshooting, Generative AI, Git, Pyspark, Kubernetes, Infrastructure Automation Frameworks, Machine Learning Operations, Terraform, Data Pipelines, Docker, Databricks
- **Published:** July 8, 2026
- **Apply:** https://uk.indeed.com/viewjob?jk=dbe77d697705a4fc

## About the Role

* 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.

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., machine learning,python,databricks,pyspark

## Description

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., * 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 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, DevOps, security, and operational teams to ensure scalable and secure deployments.
 * Create deployment documentation, runbooks, and support processes.


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