Job description
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
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 automation
- 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 pipelines
- 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