Top AI Jobs in Dublin

Conduct advanced research in artificial intelligence and machine learning, including deep learning, graphical models, reinforcement learning, computer perception, natural language processing, and data representation. Define complex business problems, design and deliver modeling solutions, evaluate model performance, establish scientific and engineering specifications, and build analytical roadmaps. Assess production implementation requirements, improve data capture methods, apply statistical and mathematical techniques, and resolve root causes and process bottlenecks.

Drive AI solution sales and digital transformation for enterprise customers in Norway. Engage customers, identify AI adoption opportunities, develop solutions through demonstrations and workshops, coordinate account teams and partners, analyze competitors, and guide opportunities toward closure and successful outcomes.

Manage enterprise customer accounts to drive product adoption, value realization, satisfaction, retention, renewals, and expansion. Build success plans, monitor customer health, conduct business reviews, manage escalations, and coordinate cross-functional teams. Serve as a trusted advisor to business and technical stakeholders while advocating for customer needs. The role requires fluent English and German and supports software, SaaS, cybersecurity, and data management customers.

Design, deliver, and continuously improve scalable production AI solutions using LLMs, RAG, agentic AI, machine learning, and document intelligence. Build cloud-native systems on Azure using software engineering, architecture, DevOps, and MLOps practices. Own AI platforms and models across deployment, monitoring, security, governance, and optimization. Lead technical initiatives, collaborate with product and business teams, mentor engineers, and translate business needs into measurable outcomes.

Lead post-sales enablement for OpenAI products, including ChatGPT Enterprise and Agents. Design and deliver onboarding, workshops, advanced training, executive briefings, and adoption programs for business and technical audiences. Drive activation, sustained usage, product expansion, and measurable business value across enterprise customers. Develop reusable training assets and playbooks, gather customer feedback, identify adoption patterns, and collaborate with Sales, Solution Architects, Delivery Leads, and product teams.

Leads Microsoft’s UK and Ireland Solution Architect and consulting team delivering AI-driven digital transformation for strategic customers. Responsibilities include people management, solution and software architecture, Azure adoption, application modernization, data platform strategy, AI and analytics design, cloud security, compliance, and network architecture. The role also involves influencing customers across industries, supporting complex transformation programs, and potentially contributing to technical sales and portfolio management.

Lead the architecture and delivery of scalable, secure cloud-native platforms across public clouds. Define reference architectures, engineering standards, governance controls, observability practices, and reusable developer-platform capabilities. Drive responsible adoption of generative and agentic AI in engineering workflows, including validation, auditability, security, and data protection guardrails. Partner with architecture, engineering, product, security, risk, and business teams while mentoring technical talent and setting enterprise architecture direction.

Establish and scale EMEA Deal Strategy while personally leading complex, high-value commercial opportunities from strategy through closure. Develop deal strategies, proposals, ROI analyses, business cases, negotiation playbooks, and reusable commercial frameworks. Partner cross-functionally with Sales, Pricing, Finance, Product, Legal, and Operations, support executive customer conversations, improve deal workflows using AI, and translate strategic deal insights into scalable regional operating models.

Leads the design and evolution of an enterprise AI platform supporting experimentation through production. Manages and mentors AI engineers, defines the technical roadmap, and develops shared capabilities such as model serving, feature stores, vector databases, AI gateways, and registries. Establishes MLOps and LLMOps standards, cloud-native infrastructure, governance, security, observability, reliability, and automation practices. Partners with architecture, cloud, data, security, product, and data science teams to scale secure, resilient, and cost-effective AI adoption.

Lead the design and evolution of an enterprise AI platform supporting experimentation through production. Manage and mentor AI engineers, define platform roadmaps, establish MLOps and LLMOps standards, and build services for model serving, inference, feature stores, vector databases, governance, and observability. Design secure cloud-native infrastructure using Kubernetes and infrastructure as code, ensure reliability and scalability, productionize AI solutions, and collaborate with architecture, security, data, product, and science teams.

Lead the design and evolution of Mastercard’s enterprise AI platform, managing and mentoring AI engineers while defining platform roadmaps, MLOps and LLMOps standards, cloud-native infrastructure, model serving, governance, security, observability, and reliability practices. Partner with architecture, cloud, data, security, product, and data science teams to productionize AI solutions and deliver scalable, secure, reusable capabilities that accelerate enterprise AI adoption.

Lead the design and operation of Mastercard’s enterprise AI platform, including model serving, inference APIs, vector databases, feature stores, governance, observability, and self-service capabilities. Manage and mentor AI engineers while defining platform strategy and technical roadmaps. Establish MLOps and LLMOps standards, build cloud-native infrastructure, and ensure scalability, reliability, security, compliance, and cost efficiency. Partner with data scientists, security, architecture, and product teams to productionize AI and generative AI solutions across the enterprise.

Lead the design and operation of an enterprise AI platform supporting production AI and Generative AI solutions. Manage and mentor AI engineers, define platform roadmaps, build shared services such as model serving and vector databases, establish MLOps and LLMOps standards, and ensure security, governance, reliability, scalability, and cost efficiency across cloud-native infrastructure. Partner with architecture, security, data, product, and data science teams to accelerate enterprise AI adoption.

Lead the design and operation of Mastercard’s enterprise AI platform, including model serving, inference APIs, feature stores, vector databases, governance, observability, and developer self-service. Manage and mentor AI engineers, establish MLOps and LLMOps standards, drive cloud-native infrastructure and reliability, and partner with architecture, security, data, product, and data science teams to productionize scalable, secure AI and Generative AI solutions.

Lead the design and operation of Mastercard’s enterprise AI platform, including model serving, inference APIs, vector databases, MLOps, LLMOps, governance, observability, and cloud-native infrastructure. Manage and mentor AI engineers, define the technical roadmap, productionize AI and generative AI solutions, and ensure platform security, scalability, reliability, compliance, and cost efficiency across the enterprise.

Leads the design and operation of Mastercard’s enterprise AI platform, including model serving, MLOps, LLMOps, cloud-native infrastructure, governance, security, observability, and reliability. Manages and mentors AI engineers, defines the platform roadmap, partners with architecture, security, data, and product teams, and enables production deployment of AI and generative AI solutions at enterprise scale.

Lead the design and operation of an enterprise AI platform supporting production AI and Generative AI solutions. Manage and mentor AI engineers, define platform roadmaps, build shared services for model serving and orchestration, establish MLOps and LLMOps standards, and ensure scalability, reliability, security, governance, and compliance. Partner with architecture, cloud, data, security, product, and data science teams to accelerate enterprise AI adoption.

Lead the design and operation of Mastercard’s enterprise AI platform, including model serving, inference APIs, vector databases, registries, and developer tools. Manage and mentor AI engineers, define the platform roadmap, establish MLOps and LLMOps standards, and ensure secure, scalable, reliable cloud-native infrastructure. Partner with architecture, security, data, product, and science teams to productionize AI solutions while implementing governance, observability, compliance, automation, and operational excellence.

Lead the design and evolution of an enterprise AI platform supporting experimentation through production. Manage and mentor AI engineers, establish MLOps and LLMOps standards, build cloud-native services for model serving and orchestration, and ensure scalability, reliability, security, governance, and cost efficiency. Partner with architecture, cloud, data, security, product, and data science teams to productionize AI and generative AI solutions while driving platform strategy, operational excellence, and engineering best practices.

Research and write high-quality content about venture capital markets, funding rounds, funds, investors, portfolio companies, and company investments. Own longer blog posts and investment-related articles, while collaborating with the Head of Brand on publishing, scheduling, and distribution across LinkedIn, the blog, newsletters, and X. The role is remote, async, and designed to develop into a VC analyst and content position.

Design, deploy, and operate production AI agents and generative AI solutions using Azure OpenAI, Snowflake, Databricks, retrieval, semantic models, and tool orchestration. Build reliable data workflows, governance, security, monitoring, evaluation, and responsible AI controls for sensitive healthcare data. Integrate AI systems with enterprise applications and APIs, troubleshoot performance and retrieval issues, and collaborate with technical and business stakeholders to deliver compliant, scalable solutions.

Lead development of software tools and AI-powered solutions that identify Windows application compatibility regressions before release. Enhance an automation framework and test-authoring experience, analyze data to guide engineering decisions, and collaborate with global teams to resolve compatibility issues across devices, silicon platforms, and Windows releases.

Manage multi-country payroll operations, compliance, audits, controls, vendor relationships, payroll analysis, and process improvements. Lead payroll system automation and scalability initiatives while ensuring accurate and timely processing across jurisdictions. Partner with finance, HR, treasury, and external providers, monitor regulatory changes, and mentor a global payroll team in a distributed technology environment.

Partner with mid-market customers to drive revenue transformation using Gong’s AI Operating System. Design AI-driven workflows, lead adoption and change management, quantify ROI, and manage renewals and expansion. The role requires executive stakeholder engagement, cross-functional collaboration, and ownership of customer activation, value realization, retention, and revenue growth.

Partner with enterprise customers and senior GTM stakeholders to drive revenue transformation using Gong’s AI Operating System. Architect AI-enabled workflows, lead adoption and change management, quantify ROI, and deliver measurable customer outcomes. Own renewal strategy and expansion opportunities while collaborating cross-functionally with Sales, Product, Support, Marketing, and executives. Success is measured through activation, adoption, value realization, retention, expansion, and revenue impact.