Principal Engineering Platform AI Architect

Overview

In this role you shape how AI is embedded across Arm’s engineering platforms and workflows, driving scalable, secure AI architectures. You will translate fast-moving AI capabilities into reusable patterns and target architectures for silicon design, software development, CI/CD, and developer tools. You lead through deep expertise, collaborating with platform, data, and security leaders to balance scale, latency, and resilience from experiment to production. You will influence across engineering teams to reduce fragmentation and promote reusable foundations. This is a senior individual contributor role that shapes the AI-enabled engineering strategy and patterns across the company.

Pay / Benefits

• hybrid working
• continuous learning and professional growth
• inclusive environment
• collaboration with industry leaders
• opportunity to impact world-class products

Responsibilities

• Define target architecture and roadmaps for AI-enabled engineering across software development, build, verification, test, design and operations.
• Decide on build vs. buy vs. reuse, platform fit and lifecycle to reduce fragmentation and vendor dependency.
• Create practical patterns for agents, retrieval, tool orchestration, context, evaluation, observability and production support.
• Define integration patterns across source control, CI/CD, artefact management, IDEs, APIs, and scheduling.
• Set architecture for identity, secrets, permissions, tool execution, approvals, traceability and safe shutdown.
• Shape AI access to code, designs, telemetry and engineering knowledge while protecting provenance and IP.
• Lead key architectural decisions for performance, reliability and cost from experiments to production.
• Collaborate with platform, data, security and service leaders to mature capability through mentoring and reusable guidance.

Key requirements

• Record of leading architecture for complex engineering platforms or technical computing systems at scale.
• Experience designing production AI solutions including generative AI and retrieval-augmented generation.
• Strong knowledge of source control, CI/CD, artefact management, build systems, APIs, IDEs and command-line workflows.
• Experience designing for distributed, compute-intensive workloads with considerations for throughput, latency, concurrency and cost.
• Knowledge of identity, authorization, secrets, privileged execution, software supply-chain controls, auditability and safe failure for AI-enabled workflows.
• Ability to translate between specialists and enterprise teams and influence senior leaders without formal authority.

• influence and leadership without direct authority
• communication across technical and non-technical stakeholders
• strategic thinking and prioritization
• AI architecture for engineering platforms
• production AI design (generative AI, agentic systems, retrieval-augmented generation)
• integration patterns for source control, CI/CD, artefact management, APIs, tooling

…