Deep expertise in Kubernetes and Linux, including network fundamentals (DNS, load balancing, high availability, firewalls) and hands-on experience operating clusters at scale
Strong proficiency with GitHub Actions, including designing and maintaining custom runners and authoring reusable workflows for large engineering organisations
Proficiency in programming languages such as Golang and Python, and shell scripting, with the ability to develop custom controllers, operators, and platform tooling
Proven experience managing declarative GitOps CD using ArgoCD and Infrastructure as Code at scale
Demonstrated ability and enthusiasm for agentic engineering using tools such as Claude Code and GitHub Copilot with proper engineering discipline
Solid understanding of container security, network policies, and observability and logging tools such as Grafana and Splunk, with experience establishing platform-wide standards 
Ability to use AI agents as collaborative partners across the full development lifecycle - design, implementation, testing, and review, not as a shortcut that bypasses engineering rigour
Ability to ensure AI-assisted output meets the same standards as hand-written code: proper test coverage, code review, documentation, and production readiness
Ability to write clear specifications, context, and constraints that direct AI agents to produce high-quality, maintainable solutions rather than throwaway prototypes
Continuous learning mindset and able to stay current with rapidly evolving agentic tooling and techniques, actively experimenting with new models and integrations to improve team productivity
Ability to apply engineering judgment to validate, test, and refine AI-generated code and configurations before they reach production environments.