The candidate should meet the following requirementsJob DescriptionRole DescriptionThe ideal candidate will haveAbout the Role We’re looking for an MLOps Engineer to help scale machine learning from experimentation to production. You’ll work closely with Data Scientists, Software Engineers, and Product teams to build robust, automated, and secure ML infrastructure that supports model deployment, monitoring, and lifecycle management.
This is an exciting opportunity to shape best practices in CI/CD for ML, reproducibility, and cloud-native model serving within a growing, data-driven organisation based in Cambridge.
Key ResponsibilitiesDesign, build, and maintain scalable ML pipelines (training, validation, deployment, monitoring)Productionise machine learning models and ensure reliability, performance, and observabilityImplement CI/CD workflows for ML using modern DevOps toolingManage containerised workloads (Docker/Kubernetes) in cloud environments (AWS/GCP/Azure)Monitor model performance, drift, and data quality in productionCollaborate with Data Science teams to improve reproducibility and experiment trackingContribute to infrastructure-as-code and platform automationRequired Skills & ExperienceStrong Python skills and experience deploying ML models to productionSolid understanding of MLOps principles and ML lifecycle managementExperience with Docker and KubernetesFamiliarity with cloud platforms (AWS, GCP, or Azure)CI/CD experience (GitHub Actions, GitLab CI, Jenkins, etc.)Experience with SQL and data pipelinesDesirableExperience with ML orchestration tools (e.g., Airflow, Kubeflow, MLflow)Knowledge of monitoring tools (Prometheus, Grafana)Infrastructure-as-Code (Terraform, CloudFormation)Experience working in regulated or high-availability environments
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