Skills

Full description

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.