Top Tech Jobs & Startup 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.

Lead end-to-end product ownership for AI-powered features for SME-focused products. Define EPICs and user stories, collaborate with engineering, design and data science, support LLM and agentic workflows, run discovery and experimentation, and steer delivery to meet scope, budget and timeline.

Lead end-to-end product ownership for AI-powered features (LLM and agentic workflows). Define EPICs and user stories, partner with engineering, design, and data science, run discovery and experiments, steer delivery to scope/budget/timeline, and communicate with technical and non-technical stakeholders.

Lead end-to-end product ownership for AI-powered solutions targeting SMEs: define EPICs and user stories, support LLM and agentic feature development, partner with engineering/design/data science, run discovery and experimentation, steer delivery to roadmap, budget, and timelines, and communicate across technical and non-technical stakeholders.