Principal AI-Native Systems Engineer

Role Purpose
The Principal AI-Native Systems Engineer is accountable for the end-to-end engineering of complex software and data platforms. They combine deep systems engineering expertise with AI-native delivery practices to design, validate and deliver solutions through small, highly capable teams augmented by AI agents.
Unlike traditional engineering leadership positions, this role remains deeply hands-on and directly engaged in requirements definition, architectural design, implementation oversight and customer outcomes. The role acts as the technical owner for systems from concept through to production operation.

AI-Native Engineering Leadership

• Define and lead AI-native software engineering practices.
• Orchestrate AI agents across design, coding, testing, documentation and operational activities.
• Establish standards for AI-assisted software delivery.
• Evaluate, validate and govern AI-generated outputs.
• Continuously improve software engineering productivity through AI-native approaches.

Requirements and Specification Engineering

• Work directly with business stakeholders and users to define solution outcomes.
• Translate business requirements into deterministic technical specifications.
• Define acceptance criteria suitable for automated validation.
• Ensure traceability between user requirements, specifications and delivered functionality.

Systems Engineering at Scale

• Design large-scale distributed systems operating across cloud-native environments.
• Define data structures, integration patterns, event-driven architectures and service interactions.
• Design for resilience, scalability, observability and fault tolerance.
• Lead engineering decisions around performance, data movement and optimisation.

Technical Product Ownership

• Own technical outcomes from concept through production adoption.
• Balance business outcomes, engineering quality, cost and risk.
• Drive prioritisation of engineering activities to maximise user value.

Essential Technical Skills

• Deep understanding of distributed systems architecture.
• Advanced knowledge of algorithms, data structures and computational complexity.
• Understanding of operating systems, CPU architecture and compilation concepts.
• Experience with cloud-scale engineering on AWS or equivalent platforms.
• Strong understanding of networking, protocols and distributed computing principles.
• Strong systems programming experience using languages such as Go, Rust, C/C++ or similar.
• Experience designing data-intensive systems and large-scale processing platforms.

AI Engineering Skills

• Experience operating within AI-assisted software delivery environments.
• Ability to leverage AI coding agents for software development and architecture activities.
• Understanding of agent orchestration patterns.
• Experience validating AI-generated code, designs and documentation.
• Ability to create engineering workflows optimised for AI-driven execution.