MLOps Platform Engineer

MLOps Platform Engineer

Location: Oxford, UK (Hybrid)

Organisation: Global AI & Health Innovation Programme

Employment Type: Full-time | Permanent

About the Role

We are seeking a MLOps Platform Engineer to design, implement, and scale the infrastructure that supports high-performance machine learning and AI-driven research workflows . You will play a critical role in bridging the gap between data science, bioinformatics, and engineering — ensuring seamless, 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 and automate workflows using Terraform, Kubernetes, Docker , and CI/CD 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

• Proven experience as an MLOps Engineer, Platform Engineer, or DevOps Engineer supporting ML or data science teams.
• Strong hands-on experience with containerization (Docker) and orchestration (Kubernetes) .
• Expertise in Terraform , Infrastructure as Code (IaC), and cloud provisioning (OCI, AWS, GCP, or Azure).
• Solid understanding of CI/CD pipelines and automated testing frameworks.
• Experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Familiarity with MLflow, Kubeflow, DVC, or similar MLOps tools .
• Understanding of cloud security principles , IAM, and networking best practices.
• Proficiency in Python and Bash scripting for automation and tooling development.
• Version control with Git , and collaborative development practices.

Desirable Experience

• Exposure to bioinformatics or health data ecosystems (WGS, transcriptomics, clinical data).
• Knowledge of data governance and compliance frameworks (GDPR, ISO27001, HIPAA).
• Experience building monitoring dashboards for ML performance metrics.
• Familiarity with distributed training environments and GPU/TPU orchestration.
• Oracle Cloud Infrastructure (OCI) certification or equivalent.

Terms of Appointment

Applicants must have the right to work permanently in the UK and be within commuting distance of Oxford . Occasional travel may be required for collaboration across global sites.