MLOps Engineer
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Develop are looking for an experienced Senior MLOps Engineer to support the development, deployment and optimisation of production-grade Machine Learning infrastructure within a large-scale enterprise environment.
This is a hands-on engineering role focused on building scalable ML platforms, automating model deployment and improving the reliability and performance of machine learning systems.
Key Responsibilities:
- Design, build and maintain scalable MLOps pipelines and infrastructure.
- Develop automated CI/CD pipelines for machine learning model training, testing and deployment.
- Deploy and manage ML models in production environments.
- Implement model monitoring, versioning, experiment tracking and performance optimisation.
- Build and maintain containerised ML workloads using Docker and Kubernetes.
- Work closely with Data Scientists, ML Engineers and Platform Engineering teams.
- Implement Infrastructure as Code (IaC) and automated cloud provisioning.
- Improve model lifecycle management, observability and platform reliability.
Essential Technical Skills:
- Strong commercial experience in MLOps or ML Platform Engineering.
- Advanced Python programming skills.
- Hands‑on experience with MLflow, Kubeflow or similar MLOps frameworks .
- Strong knowledge of Docker and Kubernetes .
- Experience building CI/CD pipelines using GitHub Actions, GitLab CI or Azure DevOps .
- Cloud experience with AWS SageMaker, Azure Machine Learning or Google Vertex AI .
- Infrastructure as Code using Terraform .
- Experience with model serving, monitoring and automated retraining pipelines.
- Understanding of ML model lifecycle management, feature stores and data pipelines.
- LLMOps and deployment of Generative AI models.
- Model serving frameworks such as KServe, BentoML or NVIDIA Triton.
- Distributed model training and inference.
- Monitoring tools such as Prometheus and Grafana.
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