Staff Engineer - AI Platform and Enablement

Job description

Role Overview This is a hybrid role based in our Cork office. As a Staff Engineer on NETGEAR’s AI Platform and Enablement team, you will play a strategic and highly impactful role in shaping how AI is built into the fabric of the whole company: engineering teams, business units and corporate functions alike. This position is designed for a deeply experienced technical leader who thrives at the intersection of AI infrastructure, developer enablement and business enablement. You will own three interconnected areas of ownership. First, you will help build and operate the core AI and LLM platform: model routing, evaluation, and the shared tooling that lets teams across NETGEAR build reliably on top of large language models. Second, you will scale the use of coding agents and assistants across engineering, raising AI fluency and the bar on how we ship software. Third, and equally important, you will extend AI enablement beyond engineering and into business functions — finding the workflows worth automating and building them. That third mandate is not a side project. A meaningful share of NETGEAR’s near-term AI value sits in business process, not product code, and this role is expected to have organisation-wide impact. Much of the work will resemble the enablement currently being driven for non-technical teams, scaled up and made repeatable. With significant autonomy, you will own delivery of key technical initiatives aligned with organisational goals and directly influence the short and medium-term success of NETGEAR’s AI strategy. Key Responsibilities AI and LLM Platform Infrastructure - Design, build, and operate the shared AI platform layer: model routing and fallback strategies, API gateway patterns, rate limiting, and multi-provider abstraction (Anthropic, OpenAI, and others as needed). - Own cost and usage optimisation across the organisation’s AI spend, covering model selection, prompt and context efficiency, caching, and session-level cost controls. - Build evaluation, observability, and monitoring for LLM-powered features and agents, including quality regression detection and usage analytics. - Define architecture and standards for secure, scalable integration of AI capabilities into product and internal systems, including authentication, data handling, and compliance considerations. Engineering AI Enablement - Drive adoption and effective use of AI coding agents and assistants (for example Claude Code, GitHub Copilot, Cursor) across engineering teams. - Build internal tooling, guardrails, and workflows (agentic SDLC patterns, managed agents, review and QA processes) that let engineers safely delegate more work to AI. - Establish best practices, documentation, and training that raise the AI fluency of the wider engineering organisation. Business and Functional Enablement - Partner directly with business units and corporate functions (Finance, Operations, Supply Chain, Marketing, Sales, People, Legal) to identify high-value AI opportunities in their day-to-day work. - Sit with functional teams, map how their processes actually run today, and simplify before automating: strip out steps that exist only for historical reasons rather than encoding them into an agent. - Design and deliver AI solutions for non-technical users, including skills, agents, connectors and internal applications that people without an engineering background can adopt and trust. - Build the enablement layer that makes this repeatable: reusable patterns, templates, onboarding material, office hours, and a community of practice across functions. - Translate fluently in both directions - turning business problems into technical designs and explaining technical constraints and risks in language a functional leader can act on. Technical Leadership and Collaboration - Reporting directly to the AI Platform Engineering Director, working with cross-functional teams to align technical execution with NETGEAR’s AI strategy. - Own delivery of end-to-end initiatives that bridge platform infrastructure, developer tooling, business process and product engineering. - Conduct architecture reviews, code reviews, and system debugging across multiple layers of the platform. - Provide mentorship and guidance to engineers and functional champions working with AI tools and platform services, nurturing a culture of technical excellence and responsible AI use. - Establish and track clear objectives, delivering measurable results aligned with quarterly goals and reporting on AI-driven productivity gains and cost efficiency across the organisation. Required Qualifications - 8+ years of software engineering experience, with a demonstrated history of leading impactful technical initiatives. - Hands-on experience building with LLM APIs (Anthropic, OpenAI, or similar) in production, including prompt and context design, tool use, and agentic workflows. - Demonstrated ability to simplify complexity: taking a