About 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 Responsibilities
Design, build, and maintain scalable ML pipelines (training, validation, deployment, monitoring)
Productionise machine learning models and ensure reliability, performance, and observability
Implement CI/CD workflows for ML using modern DevOps tooling
Manage containerised workloads (Docker/Kubernetes) in cloud environments (AWS/GCP/Azure)
Monitor model performance, drift, and data quality in production
Collaborate with Data Science teams to improve reproducibility and experiment tracking
Contribute to infrastructure-as-code and platform automationRequired Skills & Experience
Strong Python skills and experience deploying ML models to production
Solid understanding of MLOps principles and ML lifecycle management
Experience with Docker and Kubernetes
Familiarity with cloud platforms (AWS, GCP, or Azure)
CI/CD experience (GitHub Actions, GitLab CI, Jenkins, etc.)
Experience with SQL and data pipelinesDesirable
Experience 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