Top AI Jobs in Dublin

Designs, develops, deploys, and supports secure enterprise applications, integrations, and AI-enabled solutions. Responsibilities include full software development lifecycle ownership, AI and LLM integration, relational database design, API development, data architecture, cloud-hosted solutions, production troubleshooting, and continuous improvement. Provides technical leadership, mentors developers, collaborates with stakeholders, and ensures applications are scalable, maintainable, secure, and aligned with business objectives.

Own the full sales cycle for startup customers across EMEA, from outbound prospecting through launch. Drive revenue and quota attainment through consultative, technical sales of Anthropic’s AI solutions. Develop market-expansion strategies, identify customer segments and use cases, negotiate complex agreements, gather feedback to inform product roadmaps, and help establish regional go-to-market processes, playbooks, and sales methodologies.

Leads customer AI and cloud transformation by designing, demonstrating, and deploying secure, scalable architectures. Translates business and technical requirements into cloud solutions, runs pilots and proofs of concept, removes technical blockers, and drives adoption, consumption, and measurable business value. Builds executive and partner relationships, coordinates cross-functional resources, supports sales engagements, delivers technical workshops, and provides architecture thought leadership across Microsoft cloud solutions.

Review and annotate images by identifying people, actions, and predefined categories. Follow detailed annotation guidelines, maintain accuracy and consistency, review completed work, and help create high-quality datasets for AI models. The role requires strong attention to detail, independent work, reliability, and EU citizenship with residence in the European Union.

Lead the Agentic Discovery product chapter, initially owning business tooling, dashboards, transparency, and controls for AI-powered commerce experiences. Define strategy, align ML, engineering, design, and go-to-market teams, scale experimentation, launch reliable LLM-powered experiences, and establish metrics tied to usage, conversion, revenue, and engagement. The role will expand to chapter-wide leadership across on-site, offsite, and integrated agentic discovery surfaces.

Develop AI-driven products through hands-on software development and product engineering. Build minimum viable products, solve problems creatively, and help shape future AI applications. The role is available remotely or in Boston and Chicago, with contract options ranging from two weeks to three months or full-time, plus potential revenue sharing.

Graduate engineering opportunities across Analog Devices’ Ireland locations in electronics, semiconductor, software, AI, robotics, manufacturing, research, quality, and supply chain. Graduates may work in IC design, embedded software, verification, RF design, process engineering, testing, packaging, reliability, and related functions. The program includes rotations, learning opportunities, relocation assistance, and access to advanced facilities. Recruitment begins in September 2026 on a rolling basis. No travel is required.

Graduate software engineering opportunities involving embedded software, software systems, software development, AI, machine learning, UI/UX design, and cloud architecture. Graduates collaborate with hardware teams on planning, system architecture, development, troubleshooting, debugging, and testing across applications including IoT, automotive radar, healthcare monitoring, and 5G. The role includes a graduate rotation program, learning opportunities, and exposure to advanced semiconductor technologies.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

Drive revenue and adoption of Zendesk’s AI products within a defined territory. Own sales quotas, prospect, conduct tailored demonstrations, support proof-of-concepts, and close new and expansion business. Partner with account executives, customer success, and regional leadership to develop sales strategies, identify cross-sell opportunities, model commercial value, and maximize automation-related revenue. Serve as an AI subject matter expert, enable sales teams, and communicate product updates and market insights.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Build and scale the APAC developer community for an open-source AI foundation. Responsibilities include developer advocacy, contributor programs, technical content, ecosystem partnerships, governance support, events, hackathons, and community metrics. The role serves as a public representative, translates complex technical concepts for developers, and collaborates with maintainers, researchers, enterprises, and open-source communities to grow adoption and participation.

Drive business development and close high-value enterprise deals with global biotech and biopharma accounts across Europe. Own strategic customer relationships, deliver recurring revenue, partner with technical and customer success teams on complex proposals, provide market insights for product direction, build industry credibility, and improve enterprise sales processes and playbooks.

Own end-to-end product marketing for Operator, Fin’s AI agent for customer operations. Develop category-defining narratives, positioning, ICP and messaging; lead launches and go-to-market initiatives; create sales enablement, competitive positioning, web content, demos and other PMM assets. Partner with product, marketing, sales and leadership, conduct customer research, translate technical capabilities into clear value, and maintain messaging systems supporting Fin’s AI-first approach.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps practices, automation, CI/CD workflows, and monitoring frameworks; address performance issues, data drift, and operational incidents. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance groups to define success metrics, meet regulatory and ethical standards, and deliver business value. Lead cross-functional initiatives and influence outcomes without direct authority.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML solutions in production. Establish MLOps automation and workflows, manage model performance and data drift, respond to incidents, and conduct root cause analysis. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical requirements, and ensure AI solutions deliver business value.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps automation and CI/CD workflows, manage incidents and root-cause analysis, and ensure model reliability, scalability, governance, compliance, and ethical operation. Partner with AI engineers, product teams, business stakeholders, and risk functions to define success metrics, translate business needs into technical requirements, and maximize the business value of AI solutions.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps practices, automation, reliability monitoring, and incident response processes. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical standards, resolve operational issues, and demonstrate business value.

Leads end-to-end deployment, operationalization, monitoring, and continuous improvement of AI/ML solutions in production. Establishes MLOps automation and workflows, manages model performance and data drift, responds to incidents, and conducts root cause analysis. Partners with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical requirements, and ensure AI deployments deliver business value.

Leads deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establishes MLOps practices, automation, reliability frameworks, and incident response processes. Partners with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical requirements, resolve performance or data-drift issues, and ensure AI solutions deliver business value.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps automation and workflows, resolve performance bottlenecks and data drift, manage operational incidents, and conduct root cause analysis. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet business objectives, and ensure deployments satisfy regulatory, security, and ethical AI requirements.

Leads the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establishes MLOps automation and CI/CD workflows, monitors performance and data drift, responds to incidents, and conducts root cause analysis. Partners with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical requirements, and ensure AI solutions deliver business value.

Leads the deployment, operationalization, monitoring, and continuous improvement of AI/ML solutions in production. Establishes MLOps automation and workflows, defines success metrics with stakeholders, manages model performance and data drift, ensures governance and compliance, and leads incident response and root-cause analysis. The role bridges technical and business teams while driving cross-functional initiatives and operational excellence.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps automation and CI/CD workflows, identify performance issues and data drift, manage incidents and root-cause analysis, and ensure reliable, scalable operations. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical requirements, and connect AI deployments to business objectives.

Lead end-to-end deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps automation and workflows, identify performance issues and data drift, lead incident response and root-cause analysis, and ensure reliable, scalable deployments. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to translate business needs into technical requirements, measure outcomes, and meet regulatory and ethical AI standards.