Platform Engineer

Responsibilities:

• Design, build, and maintain secure, scalable, and highly available cloud infrastructure.
• Manage Kubernetes and containerised environments for distributed applications and AI/ML workloads.
• Build and optimise high-performance compute infrastructure, including GPU environments, for LLM serving and model training.
• Develop and manage MLOps infrastructure to support model deployment, serving, monitoring, and lifecycle management.
• Design and maintain Airflow-based data orchestration workflows for data and ML pipelines.
• Implement CI/CD and GitOps pipelines to automate application, microservice, and ML model deployments.
• Manage Infrastructure as Code (IaC) using tools such as Terraform.
• Set up comprehensive logging, monitoring, alerting, and observability for infrastructure, APIs, and ML systems.
• Monitor model performance and drift and support timely remediation.
• Implement cloud security, IAM, access controls, secrets management, and data governance.
• Ensure infrastructure follows relevant healthcare/security compliance requirements.
• Optimize cloud infrastructure, GPU utilisation, compute resources, and LLM/token costs.
• Collaborate with ML engineers, data engineers, software engineers, and security teams to build reliable AI platforms.
• Troubleshoot and resolve production infrastructure, deployment, performance, and scalability issues.
• Continuously improve platform reliability, automation, scalability, and operational efficiency.