Principal AI-Native Systems Engineer
Posted Updated 2
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Job Summary (List Format):
Role Accountabilities:
- Define end-to-end architecture for AI-native, cloud-native, and data-intensive systems, including system boundaries, interfaces, and operational controls.
- Translate business problems into technical specifications, acceptance criteria, and non-functional requirements through direct engagement with users and stakeholders.
- Orchestrate and utilize AI agents to accelerate software design, coding, testing, documentation, infrastructure, and deployment, maintaining human accountability for quality.
- Establish and enforce reusable engineering standards, templates, prompts, review criteria, and governance for AI-assisted software delivery.
- Design scalable, secure, and resilient distributed systems across cloud platforms, APIs, data pipelines, and event-driven architectures.
- Ensure quality and production readiness by collaborating with Test Engineers to meet functional, security, performance, and operational requirements.
- Communicate complex technical concepts clearly to technical and non-technical stakeholders.
- Coach engineers on systems thinking, AI-native engineering practices, software fundamentals, and technical decision-making.
- Drive continuous improvement by integrating delivery feedback, incident insights, and cost analysis into better specifications and workflows.
Skills & Experience Required:
- Extensive experience designing complex software, distributed systems, data platforms, or cloud-native services.
- Practical experience with AI-assisted engineering tools or agents for software delivery and productivity.
- Strong understanding of algorithms, data structures, concurrency, operating systems, networking, APIs, and integration/performance trade-offs.
- Solid knowledge of cloud platforms (preferably AWS), including architecture, security, observability, deployment, and cost management.
- Proficiency in system-oriented programming languages (Go, Rust, C/C++, Java); Python experience is a plus.
- Proven leadership in architecture and design, guiding teams through ambiguity and complex trade-offs.
- Experience embedding secure, test-first, observable, and compliant engineering practices.
- Excellent communication skills bridging business needs and technical solutions.
- Quick learning ability and curiosity for underlying system mechanics.
Experience Expected:
- Strong background in software architecture, systems engineering, distributed systems, or complex cloud-native platform delivery.
- Experience owning technical designs from problem framing to production release and support.
- In-depth understanding of AI-assisted engineering tools and governance of AI-generated artefacts.
- Experience with cloud services, CI/CD, Infrastructure as Code, observability, security, and production support.
- Ability to clarify requirements and acceptance criteria directly with users.
- (Desirable) AWS Solutions Architect certification or equivalent experience.
- (Desirable) Experience in telecoms, network operations, RAN/Core analytics, data engineering, AIOps, anomaly detection, RCA, or automation.
- (Desirable) Familiarity with event-driven architectures, lakehouse patterns, streaming data, model-assisted development, or agent orchestration.
Role Accountabilities:
- Define end-to-end architecture for AI-native, cloud-native, and data-intensive systems, including system boundaries, interfaces, and operational controls.
- Translate business problems into technical specifications, acceptance criteria, and non-functional requirements through direct engagement with users and stakeholders.
- Orchestrate and utilize AI agents to accelerate software design, coding, testing, documentation, infrastructure, and deployment, maintaining human accountability for quality.
- Establish and enforce reusable engineering standards, templates, prompts, review criteria, and governance for AI-assisted software delivery.
- Design scalable, secure, and resilient distributed systems across cloud platforms, APIs, data pipelines, and event-driven architectures.
- Ensure quality and production readiness by collaborating with Test Engineers to meet functional, security, performance, and operational requirements.
- Communicate complex technical concepts clearly to technical and non-technical stakeholders.
- Coach engineers on systems thinking, AI-native engineering practices, software fundamentals, and technical decision-making.
- Drive continuous improvement by integrating delivery feedback, incident insights, and cost analysis into better specifications and workflows.
Skills & Experience Required:
- Extensive experience designing complex software, distributed systems, data platforms, or cloud-native services.
- Practical experience with AI-assisted engineering tools or agents for software delivery and productivity.
- Strong understanding of algorithms, data structures, concurrency, operating systems, networking, APIs, and integration/performance trade-offs.
- Solid knowledge of cloud platforms (preferably AWS), including architecture, security, observability, deployment, and cost management.
- Proficiency in system-oriented programming languages (Go, Rust, C/C++, Java); Python experience is a plus.
- Proven leadership in architecture and design, guiding teams through ambiguity and complex trade-offs.
- Experience embedding secure, test-first, observable, and compliant engineering practices.
- Excellent communication skills bridging business needs and technical solutions.
- Quick learning ability and curiosity for underlying system mechanics.
Experience Expected:
- Strong background in software architecture, systems engineering, distributed systems, or complex cloud-native platform delivery.
- Experience owning technical designs from problem framing to production release and support.
- In-depth understanding of AI-assisted engineering tools and governance of AI-generated artefacts.
- Experience with cloud services, CI/CD, Infrastructure as Code, observability, security, and production support.
- Ability to clarify requirements and acceptance criteria directly with users.
- (Desirable) AWS Solutions Architect certification or equivalent experience.
- (Desirable) Experience in telecoms, network operations, RAN/Core analytics, data engineering, AIOps, anomaly detection, RCA, or automation.
- (Desirable) Familiarity with event-driven architectures, lakehouse patterns, streaming data, model-assisted development, or agent orchestration.