AI-Native Systems Engineer

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.

Additional Key ResponsibilitiesAI-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.

Enhanced Skills & ExperienceEssential 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.