Top AI & Machine Learning Jobs in Dublin

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 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 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, 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.

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.

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 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.

Embed with Services teams to write production code, unblock delivery, and deploy AI-first solutions (LLMs, agents, RAG). Provide surge engineering, clear architectural and delivery bottlenecks, coach teams on AI practices, and deliver reusable patterns and reference implementations that scale across the organization.

Embed into Services teams as a hands-on Principal engineer to write production code, unblock delivery, provide surge capacity, design and deploy production-grade AI solutions (assistants, agents, RAG), coach teams on AI-first practices, transfer knowledge, and create reusable AI patterns aligned to enterprise standards.