We are partnering with a high performing data and engineering team who are looking to add a Machine Learning Engineer or MLOps Engineer to help scale and productionise machine learning solutions across the business. This role is highly engineering focused and is ideal for someone who enjoys taking models from experimentation through to robust, monitored, and repeatable production systems. You will work closely with data scientists, platform engineers, and cloud teams to ensure ML solutions are reliable, secure, and easy to maintain. Key responsibilities Build, deploy, and support machine learning models in production environments Design and maintain end to end ML pipelines using modern ML platforms Write high quality Python and PySpark code using production software engineering practices Manage infrastructure using config driven Terraform Implement and maintain CI/CD pipelines for ML workflows Support model serving using scalable and well governed approaches Produce clear and structured technical documentation Collaborate closely with data science, engineering, and platform stakeholders Ideal experience 2 to 4 years experience in ML Engineering or MLOps roles Strong Python and PySpark skills with a production mindset Experience working with ML platforms such as Azure ML, MLflow, or equivalent Experience with config driven Terraform development Strong understanding of containerisation and orchestration CI/CD experience using GitHub Actions or Azure DevOps Strong documentation and communication skills Core tooling Containerisation, Docker, Kubernetes (AKS) or Azure Container Apps CI/CD, GitHub Actions and Azure DevOps Pipelines Infrastructure, Terraform Model serving, Azure ML Endpoints or FastAPI ML orchestration, MLflow and Azure ML pipelines Nice to have Experience with Datadog or similar observability tooling This role suits someone who cares about engineering quality, scalability, and building ML systems that deliver real business value.
