E-Solutions
Principal AI-Native Systems Engineer
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The Principal AI-Native Systems Engineer
The Principal AI-Native Systems Engineer is a senior technical leadership role within DD Ops, accountable for designing and engineering complex cloud-native, data-intensive and AI-enabled systems that support network operations outcomes across RAN, Core, data and automation domains.
This role matters because Telco is moving from large, coordination-heavy delivery models towards small expert engineering pods that use AI agents as force multipliers. The Principal AI-Native Systems Engineer owns the system engineering discipline, translates business problems into robust technical designs, and ensures that AI-assisted delivery remains governed, secure, testable and outcome-focused.
The role is hands-on and accountable for technical outcomes from concept to production. It works directly with users, architects, product owners, software engineers, test engineers and technical business analysts to ensure the right solution is specified, built, validated and operated.
What you'll be doing - Role Accountabilities
- 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.
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What you'll need to succeed - Skills & Experience
- 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.
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Experience you'd be expected to have
Essential
- Strong background in software architecture, systems engineering, distributed systems or complex cloud-native platform delivery.
- Experience owning complex technical designs from early problem framing through production release and operational support.
- Demonstrable understanding of AI-assisted engineering tools and how to validate, govern and improve AI-generated engineering artefacts.
- Experience working with cloud services, CI/CD, Infrastructure as Code, observability, security controls and production support models.
- 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.
- Experience in telecoms, network operations, RAN/Core analytics, data engineering, AIOps, anomaly detection, RCA or operational automation.
- Experience with event-driven architectures, lakehouse patterns, streaming data, model-assisted development or agent orchestration patterns.
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