Top AI Jobs in Dublin

Build and ship AI-driven GTM systems: outbound lead engine, inbound/lifecycle automations, AI-assisted content pipeline, data/reporting layer, and fast production patches to eliminate manual marketing workflows.

Own end-to-end product for a slice of Nebius AI compute platform: define strategy, APIs, and roadmaps; drive cross-team delivery; conduct structured discovery; measure adoption and platform outcomes; engage engineering as a technical peer; and close operational feedback loops to improve reliability and customer experience.

Drive the expansion of software development and AI services by managing client relationships, building sales pipelines, and negotiating contracts.

Design and build applications using Python while supporting component design, optimizing performance, collaborating with clients, and working in an agile environment.

Design, build and validate high-value AI proofs of concept and prototypes, focusing on practical business value, production-readiness and responsible AI. Collaborate with product, engineering, governance and stakeholders to run RAG, embeddings, prompt engineering and model evaluation, then recommend pilots or productionisation while ensuring security, observability, integration and operational feasibility.

Design AI behaviors, workflows, and user experiences, ensuring systems function reliably and integrate into user workflows. Collaborate with teams to define AI capabilities and evaluate system performance.

Teach and mentor experienced engineers transitioning to system architecture: run live sessions and office hours, review student architecture deliverables, lead architecture review boards, provide written feedback, record instructional videos, and support a team of instructors. Focus areas include system design, APIs, cloud/platform, distributed systems, security, and AI systems design.

Own and scale full-funnel B2B demand generation using paid acquisition, ABM, field marketing, landing pages, email funnels, and AI-driven automation (LLM agents). Measure and optimize MQLs, CPL, CAC, and pipeline contribution while partnering with Sales, Content, and Design to drive qualified pipeline.

Build and ship LLM- and agent-powered product experiences for North, designing interfaces, evals, observability, and feedback loops to improve reliability and user outcomes. Collaborate with modelling, product, design, and customers to turn failures and feedback into measurable product and model improvements.

Lead applied research and system design for agent-native search: build multi-stage retrieval, ranking, and grounding methods for LLM-driven, iterative agent workflows; deploy high-throughput, low-latency production systems; define evaluation metrics; run experiments and mentor engineers.

Lead Cohere's policy engagement on compute infrastructure, export controls, sovereign AI, and national AI competitiveness. Monitor and analyze relevant developments, develop policy positions, engage governments and cross-functional teams, represent Cohere in public discussions, and produce policy analysis and thought leadership to inform strategic business opportunities and public-private partnerships.

Join a client-facing squad as a hands-on Senior Full-Stack Engineer working across Kotlin backends and React/TypeScript frontends. Apply AI-native delivery practices and agent/harness engineering to accelerate delivery, unblock the team, and share patterns across the organisation while using AWS and cloud data tooling.

Lead testing strategy for AI-powered legal products: build evaluation frameworks, design test plans, measure AI outputs, run regression and UAT, identify risks, and partner with engineering to improve quality and scale the testing function.

Build core AI platform capabilities (agent runtimes, model routing, tool registries) and production-grade services and tooling using Node.js, TypeScript, NestJS and React. Develop reusable AI infrastructure, work with LLMs and LangChain/LangGraph, and own features end-to-end to enable secure, scalable AI across the company.

The Software Engineer will shape AI governance in developer tooling, build product features, collaborate with teams, support users, and innovate rapidly.

The AI Platform Engineer leads the development of production AI agents and components, collaborates on AI implementations, and maintains AI systems for reliability and observability.

Research and engineer large multimodal and reinforcement-learning models for robotic agents: design, train, evaluate models; prototype in simulation and on hardware; build data pipelines, simulation environments, and distributed training infrastructure; collaborate to scale promising approaches into production and publish results.

Design and build research prototypes and production-capable systems to enable portable, interoperable agent and model workflows. Develop model routing, provider abstractions, memory and context representations, benchmarking suites, and evaluation protocols. Run ML experiments, implement robust APIs and integration layers, collaborate across research and engineering, and communicate results via reports, demos, publications, and open-source releases.

Onboard and support client projects onto Nebius' Azure AI platform using runbooks and golden paths. Adapt Terraform modules and CI/CD pipelines, support ML workload deployment and lifecycle management, capture delivery gaps, and provide structured feedback to Platform Engineering while maintaining security and governance.

Lead generative AI image production for realistic personas and models. Build and optimize scalable generative pipelines, automate batch workflows, evaluate new models/tools, define technical direction and best practices, and improve image quality, consistency, speed, and cost-efficiency.

The AI Inference Engineer optimizes large language models for various environments, focusing on performance, scalability, and reliability in AI inference serving.

The AI Infrastructure Engineer will develop systems for model training and inference, optimize GPU performance, and mentor junior engineers, contributing to AI products at scale.

As Engineering Manager for AI Models Infrastructure, you will lead a team responsible for building foundational infrastructure for AI products while contributing directly to production code and enhancing developer experience.

The Engineering Manager will lead the AI Platform Team, overseeing infrastructure development, hands-on coding, and supporting ML teams while planning high-impact roadmaps and improving developer experience.

Join Intercom's AI Group as a Staff Product Engineer to build AI-powered products, collaborating with ML Scientists and product teams, and driving software development from ideation to implementation.