Top AI & Machine Learning Jobs in Dublin

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.

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.

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.