As a Senior DevOps Engineer specialising in AI and Data, you will be key to architecting and maintaining the infrastructure that supports large-scale AI/ML workloads. Your main responsibilities will be:

• Platform Deployment and Management: Design, build, and maintain scalable and secure cloud infrastructure for AI and Big Data solutions, ensuring high availability and performance in a production environment.

• AI/ML Infrastructure Automation: Implement end-to-end automation for the complete Machine Learning (ML) lifecycle, including infrastructure for model training, real-time inference, and data processing.

• Infrastructure as Code (IaC): Develop, manage, and optimise Infrastructure as Code (IaC) using tools like Terraform to provision and configure cloud resources consistently and efficiently.

• CI/CD Pipeline Development: Create, manage, and improve robust Continuous Integration and Continuous Delivery (CI/CD) pipelines to enable rapid and safe deployment of code, models, and infrastructure changes.

• Kubernetes for AI/Data: Manage and optimise Kubernetes clusters (e.g., AKS, EKS, or GKE) specifically tailored for running containerised data processing frameworks and GPU-enabled ML workloads at scale.

• Monitoring and Observability: Establish advanced monitoring, logging, and alerting systems to ensure the health, performance, and cost efficiency of the data and AI platforms.

• Security and Governance: Implement security measures, access controls, and compliance frameworks to ensure data privacy, model governance, and adherence to security best practices across all environments.

• Cross-Functional Collaboration: Work closely with Data Scientists, AI Engineers, and Software Development teams to translate complex AI requirements into robust, deployable infrastructure solutions.