Principal AI - Native Systems Engineer

What you'll be doing - Role Accountabilities

What you'll need to succeed - Skills & Experience

Own system architecture : You will define the end-to-end architecture for AI-native, cloud-native and data-intensive systems, including system boundaries, interfaces, data contracts, resilience patterns and operational controls.

Lead specification-first engineering : You will work directly with users and stakeholders to turn business problems into deterministic technical specifications, acceptance criteria and non-functional requirements.

Direct AI-native delivery : You will use and orchestrate AI agents to accelerate design, coding, testing, documentation, infrastructure and deployment activities while retaining human accountability for correctness and quality.

Set engineering standards : You will define reusable engineering patterns, guardrails, templates, prompts, review criteria and governance practices for AI-assisted software delivery.

Design distributed systems : You will design scalable, secure and resilient systems across cloud platforms, APIs, event-driven architectures, data pipelines and observability patterns.

Assure quality and production readiness : You will partner with AI-Native Test Engineers to ensure generated and human-authored outputs meet functional, security, performance, resilience and operational acceptance criteria.

Influence and communicate : You will translate complex technical concepts into clear language for senior stakeholders, engineers and business users.

Coach and develop capability : You will coach engineers in systems thinking, AI-native engineering practices, software fundamentals and accountable technical decision-making.

Drive continuous improvement : You will convert delivery learning, incident feedback, cost insights and production signals into improved specifications, agent workflows and reusable delivery accelerators.

Systems engineering depth : Extensive experience designing complex software systems, distributed systems, data platforms or cloud-native services.

AI-native engineering mindset : Practical experience using AI-assisted engineering tools or coding agents to improve software delivery, testing, documentation or design productivity.

Technical fundamentals : Strong knowledge of algorithms, data structures, concurrency, operating systems, networking, APIs, integration patterns and performance trade-offs.

Cloud and platform experience : Good knowledge of cloud compute platforms, preferably AWS, including architecture patterns, security, observability, deployment and cost considerations.

Programming capability : Comfortable with system-oriented or strongly engineered languages such as Go, Rust, C/C++, Java or equivalent; Python experience is desirable for data and automation use cases.

Architecture and design leadership : Proven ability to set technical direction, resolve complex design trade-offs and guide engineering teams through ambiguity.

Quality and governance : Experience embedding secure-by-design, test-first, observable and compliant engineering practices into delivery workflows.

Communication : Able to communicate clearly with users, engineers, product owners and senior leaders, bridging business outcomes and technical decisions.

Learning agility : Demonstrates curiosity about how systems work beneath abstraction layers and the ability to learn new technologies quickly.

Experience you'd be expected to have

Essential : Strong background in software architecture, systems engineering, distributed systems or complex cloud-native platform delivery.

Essential : Experience owning complex technical designs from early problem framing through production release and operational support.

Essential : Demonstrable understanding of AI-assisted engineering tools and how to validate, govern and improve AI-generated engineering artefacts.

Essential : Experience working with cloud services, CI/CD, Infrastructure as Code, observability, security controls and production support models.

Essential : Ability to work directly with users to clarify requirements, define acceptance criteria and ensure delivered outcomes match the original need.

Desirable : AWS Solutions Architect Associate or Professional certification, or equivalent cloud architecture experience.

Desirable : Experience in telecoms, network operations, RAN/Core analytics, data engineering, AIOps, anomaly detection, RCA or operational automation.

Desirable : Experience with event-driven architectures, lakehouse patterns, streaming data, model-assisted development or agent orchestration patterns.