Top Tech Jobs & Startup Jobs in Dublin

Own the core product experience for roadmapping, prioritization, and customer engagement. Set strategy, roadmap, metrics, and AI-first workflows; determine how Spark integrates with structured product interfaces; lead cross-team initiatives; partner with engineering on platform foundations; prototype using AI coding tools; engage customers; and mentor PMs. The role requires deep enterprise B2B SaaS, agentic product, systems thinking, research, information architecture, and technical product leadership experience.

Own the enterprise AI stack for Productboard Spark, including permissions, audit, usage limits, model routing, BYOM, automation, and governance. Define strategy and roadmap, translate enterprise customer needs into scalable product capabilities, lead cross-functional initiatives, establish success metrics, prototype with AI coding tools, and support enterprise rollouts. The role requires deep experience shipping agentic AI products, enterprise B2B SaaS, governance controls, and large-scale customer deployments.

Own the strategy, roadmap, quality, and end-to-end customer experience for Productboard Spark’s AI Insights and agentic product capabilities. This includes designing agent behavior, data and retrieval pipelines, evaluation frameworks, interactive outputs, and product metrics. The role requires hands-on prototyping, analysis of model traces and evals, customer research, cross-functional leadership, and mentoring PMs while partnering closely with the founders.

Own Productboard’s product-wide UX and interaction architecture for its AI-first product. Define patterns for agentic workflows, structured UI, generated artifacts, navigation, and object models; establish design standards and language; raise usability and coherence across teams; lead critiques and mentor designers; and personally design complex foundational experiences. Prototype in code using AI tools, validate with users, and partner with Product, Engineering, and executives to guide product direction.

Lead the AI architecture for Productboard Insights, designing agent-native systems, context infrastructure, self-healing workflows, evaluation processes, and AI code-review optimization. Own technical direction across Insights, Spark, and platform teams; drive features from concept through beta; optimize quality, latency, cost, and reliability; write production code; and develop engineers through documentation and reviews.

Own the architecture enabling AI-first product engineering, including agent-native standards, context infrastructure, self-healing incident workflows, autonomous agent features, and AI code review optimization. Ship full-stack product features, set technical direction, and partner with product and design teams. The role requires strong backend engineering, production LLM experience, distributed systems expertise, and practical use of AI coding tools.

Lead the architecture and implementation of AI-native product engineering systems, including agent-ready code standards, context infrastructure, self-healing incident workflows, multi-step agentic features, and AI code-review optimization. Ship end-to-end product features, set technical direction, evaluate LLM reliability and trade-offs, and accelerate validated learning through close collaboration with engineering, product, and design teams.

Lead architecture and end-to-end development of AI-first product features. Establish agent-native engineering standards, context infrastructure, self-healing incident workflows, multi-step agent systems, and AI code-review optimization. Collaborate with engineers, product managers, and designers to ship validated product improvements quickly while setting technical direction. The role requires strong backend and full-stack development, distributed systems expertise, production LLM experience, and effective use of AI coding tools.

Lead architectural work for AI-native backend engineering, including agent-ready code standards, context infrastructure, self-healing incident workflows, multi-step LLM agents, and AI code-review optimization. Ship backend code and AI-first product features while setting technical direction and collaborating with product and design teams. Apply strong distributed-systems, event-driven architecture, and production LLM expertise to accelerate validated learning.

Lead the architecture and implementation of AI-native product engineering systems, including agent-ready code standards, context infrastructure, self-healing incident workflows, multi-step agentic features, and AI code-review optimization. Ship production software, establish technical direction, and partner with product and design teams to accelerate validated learning. The role requires strong backend and distributed-systems expertise, hands-on LLM product experience, and practical use of AI coding tools.