Summary: The ML Operations Engineer (MLOps) role focuses on building and maintaining the infrastructure necessary for the reliable training, deployment, and monitoring of machine learning models at scale. This position serves as a bridge between machine learning research and production systems, ensuring that models are operationalized effectively. The engineer will design ML pipelines, implement CI/CD workflows, and collaborate with AI engineers and data teams. Strong experience with cloud platforms and proficiency in Python and ML tooling are essential for success in this role.
Key Responsibilities:
- Design and manage ML pipelines for training, testing, and deployment.
- Build CI/CD workflows for machine learning models.
- Deploy and monitor models in production environments.
- Ensure model reliability, scalability, and performance.
- Implement monitoring, logging, and alerting for ML systems.
- Collaborate with AI engineers and data teams to operationalise models.
Key Skills:
- Strong experience with cloud platforms (AWS, GCP, or Azure).
- Proficiency in Python and ML tooling.
- Experience with containerisation (Docker) and orchestration (Kubernetes).
- Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, or similar).
- Understanding of model lifecycle management and deployment best practices.
Salary (Rate): undetermined
City: undetermined
Country: undetermined
Working Arrangements: undetermined
IR35 Status: undetermined
Seniority Level: undetermined
Industry: Other
We are seeking an ML Operations Engineer (MLOps) to build and maintain the infrastructure that enables reliable training, deployment, and monitoring of machine learning models at scale. You’ll bridge the gap between ML research and production systems.
Responsibilities:
- Design and manage ML pipelines for training, testing, and deployment.
- Build CI/CD workflows for machine learning models.
- Deploy and monitor models in production environments.
- Ensure model reliability, scalability, and performance.
- Implement monitoring, logging, and alerting for ML systems.
- Collaborate with AI engineers and data teams to operationalise models.
Requirements:
- Strong experience with cloud platforms (AWS, GCP, or Azure).
- Proficiency in Python and ML tooling.
- Experience with containerisation (Docker) and orchestration (Kubernetes).
- Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, or similar).
- Understanding of model lifecycle management and deployment best practices.
