Job Description
3x per week office based.
We’re working with a venture-backed robotics company to find Machine Learning DevOps Engineer, you’ll sit at the intersection of machine learning, infrastructure, and product delivery. Working closely with R&D and Product teams, you’ll be instrumental in building scalable ML systems that power advanced robotic platforms.
* Build and maintain end-to-end CI/CD pipelines for ML model training, testing, and deployment
* Track model performance, accuracy, and latency; identify data and concept drift
* Design and optimise scalable cloud and on-prem environments (AWS, GCP, Azure), using Docker and Kubernetes
* Develop and maintain dashboards for live system configuration, monitoring, and fleet management
* Support data ingestion, preprocessing, and feature store development
* Implement robust versioning across models, data, and code, ensuring compliance and reproducibility
* Work closely with data scientists to productionise ML models into API-driven services
Requirements
1. 4–7 years’ experience in MLOps, ML Engineering, or DevOps
2. Strong Python skills, with Bash/shell scripting experience
3. Full-stack experience (React, TypeScript, Express.js, PostgreSQL)
4. Familiar with ML frameworks (TensorFlow, PyTorch, Scikit-learn)
5. Experience with cloud platforms (AWS SageMaker, Azure ML, or Vertex AI)
6. Solid knowledge of Docker and Kubernetes
7. Exposure to MLOps tools (MLflow, Kubeflow, Airflow, DVC)
8. Degree in Computer Science, Data Science, or similar
9. Strong problem-solving and communication skills
Benefits
* £65,000-£85,000 salary
* Share options after 12 months
* 25 days annual leave + bank holidays
Requirements
3–7 years’ experience in MLOps, ML Engineering, or DevOps roles Strong Python skills, plus experience with Bash/shell scripting Experience with full-stack development (React, TypeScript, Express.js, PostgreSQL) Familiarity with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn Experience with cloud platforms (e.g. AWS SageMaker, Azure ML, Vertex AI) Strong knowledge of Docker and Kubernetes Exposure to MLOps tools such as MLflow, Kubeflow, Airflow, or DVC Degree in Computer Science, Data Science, or a related field Strong problem-solving and communication skills