2 of 2 Remote Model Validation Jobs in Oxfordshire

secure, and reproducible deployment of ML models in production and research environments. You’ll collaborate closely with AI Scientists, Data Engineers, and DevSecOps teams , building automation pipelines that accelerate model development and deployment across distributed, cloud-native systems. Key Responsibilities Design, implement, and maintain end-to-end MLOps pipelines for model training, validation, deployment, and monitoring. Develop … toolchains (GitHub Actions, Jenkins, Argo, etc.). Manage scalable cloud-based compute environments (Oracle Cloud, AWS, or GCP) for AI workloads and data processing. Build and maintain feature stores, model registries, and versioning systems to ensure traceability and reproducibility. Implement data ingestion and pre-processing pipelines to support ML and bioinformatics workloads. Collaborate with security and DevSecOps teams to … enforce best practices in access control, compliance, and governance . Support AI/ML researchers in model experimentation and infrastructure optimization. Monitor model and system performance, implement drift detection, and refine automated retraining pipelines. Contribute to the development of a modular, reusable ML platform architecture supporting multi-modal data (genomic, clinical, imaging, etc.). Essential Skills and Experience More ❯

secure, and reproducible deployment of ML models in production and research environments. You’ll collaborate closely with AI Scientists, Data Engineers, and DevSecOps teams , building automation pipelines that accelerate model development and deployment across distributed, cloud-native systems. Key Responsibilities Design, implement, and maintain end-to-end MLOps pipelines for model training, validation, deployment, and monitoring. Develop … toolchains (GitHub Actions, Jenkins, Argo, etc.). Manage scalable cloud-based compute environments (Oracle Cloud, AWS, or GCP) for AI workloads and data processing. Build and maintain feature stores, model registries, and versioning systems to ensure traceability and reproducibility. Implement data ingestion and pre-processing pipelines to support ML and bioinformatics workloads. Collaborate with security and DevSecOps teams to … enforce best practices in access control, compliance, and governance . Support AI/ML researchers in model experimentation and infrastructure optimization. Monitor model and system performance, implement drift detection, and refine automated retraining pipelines. Contribute to the development of a modular, reusable ML platform architecture supporting multi-modal data (genomic, clinical, imaging, etc.). Essential Skills and Experience More ❯