Adroit People Limited (UK)
Artificial Intelligence Engineer
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Greetings!
We are hiring for a Principal AI-Native Systems Engineer position in London, UK.
Principal AI-Native Systems Engineer
Telecom
London
6 Months
3 days
7 Years
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.
- 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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Requirements
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- 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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