Top AI & Machine Learning Jobs in Dublin

Leads the design, development, deployment, and evaluation of machine learning solutions, including predictive models, recommendations, forecasting, fraud detection, and customer analytics. Defines experimentation frameworks, applies foundation-model embeddings, measures business impact, partners across functions, presents technical recommendations, and mentors data scientists and AI engineers. The role also contributes to hiring, capability development, and the AI organization’s technical direction.

Leads the design, development, deployment, and evaluation of machine learning solutions for forecasting, recommendations, propensity modeling, fraud detection, and customer analytics. Defines modeling and experimentation strategies, applies foundation-model embeddings, measures business impact, partners with cross-functional stakeholders, and mentors data scientists and AI engineers. The role also contributes to technical direction, hiring, and capability development.

Leads the design, development, deployment, and evaluation of machine learning solutions, including predictive models, recommendation systems, forecasting, embeddings, and foundation-model applications. Defines experimentation frameworks, metrics, and technical direction while partnering with business, product, engineering, and analytics teams. Presents insights to senior stakeholders, mentors data scientists and AI engineers, and drives projects from problem definition through measurable business impact and production deployment.

Lead machine learning and data science initiatives from problem definition through production deployment and business impact measurement. Develop predictive models, recommendation systems, forecasting, fraud detection, and customer analytics solutions using modern machine learning and foundation-model techniques. Establish experimentation and evaluation frameworks, partner with cross-functional stakeholders, present technical recommendations, mentor data scientists and AI engineers, and shape the organization’s technical direction.

Leads the design, development, deployment, and evaluation of machine learning solutions, including predictive modeling, forecasting, recommendations, fraud detection, embeddings, and foundation-model applications. Defines experimentation frameworks and success metrics, drives projects from conception through production, partners with cross-functional stakeholders, presents technical recommendations, and mentors data scientists and AI engineers. Contributes to hiring, capability development, and the organization’s long-term technical direction.

Leads the design, development, deployment, and evaluation of advanced machine learning solutions, including forecasting, recommendation, propensity, fraud, and customer analytics models. Defines experimentation frameworks, applies embeddings and foundation-model techniques, measures business impact, and partners with business, product, engineering, and analytics teams. Mentors data scientists and AI engineers, influences technical direction, presents findings to leadership, and contributes to hiring and capability development.

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.

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.

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.