Top AI & Machine Learning Jobs in Dublin

Lead design and deliver production-ready agentic AI applications and cloud-native services. Define engineering patterns for reliability, observability, security, and cost control. Drive end-to-end SDLC, mentor engineers, collaborate with AI, product, and platform teams, and operationalize AI solutions from prototype to governed production.

Lead hands-on design and delivery of agentic AI applications and platform capabilities. Define engineering patterns for production AI systems, drive end-to-end SDLC, improve AI-enabled developer workflows, ensure security, observability, resiliency and cost control, mentor engineers, and collaborate with product, data science, and security teams to move prototypes to governed production.

Lead hands-on design and delivery of production-ready, secure, scalable agentic AI applications. Define engineering patterns, drive SDLC from prototype to production, improve AI-enabled developer workflows, mentor engineers, and collaborate with product, data science, security, and platform teams to deliver measurable business value.

Lead strategy, architecture, and delivery of a production-grade data platform enabling agentic AI and analytics. Own lakehouse, streaming, retrieval, vector/feature stores, MCP servers, governance, and real-time pipelines; partner with Applied AI and Product; guide senior engineers and set standards for AI-ready data products.

Lead strategy and delivery of the data platform powering analytics and agentic AI across business verticals. Architect lakehouse, streaming, retrieval, vector stores, feature stores, and knowledge graphs. Ensure governance, lineage, data quality, observability, and AI-specific controls. Partner with Applied AI, Product, and Architecture, set standards for data products, and guide a team of senior data engineers while staying hands-on to unblock technical challenges.

Lead strategy, architecture, and delivery of CNPF data platform to power analytics and agentic AI. Design lakehouse, streaming, retrieval, vector/feature stores, MCP servers, and knowledge graphs. Ensure governance, lineage, data quality, observability, and secure AI consumption. Partner with Applied AI and Product, guide senior engineers, and remain hands-on to unblock architecture and production issues.

Lead strategy, architecture, and delivery of a production-scale data platform enabling agentic AI and analytics. Design lakehouse, streaming, retrieval, vector/feature stores, MCP servers, and governance to ensure real-time, secure, and auditable data for LLMs and analytics. Guide senior engineers and partner with AI, product, and security teams.

Lead strategy, architecture, and delivery of a production-scale data platform enabling agentic AI and analytics. Build lakehouse, streaming, retrieval, vector/feature stores, MCP servers, and knowledge graphs. Ensure governance, lineage, data quality, observability, and AI-specific controls. Partner with Applied AI, Product, and Architecture; set standards for data products exposed to agents; remain hands-on to unblock teams and grow senior data engineering talent.

Lead strategy, architecture, and delivery of Mastercard's data platform for analytics and agentic AI. Design lakehouse, streaming, retrieval, vector/feature stores, MCP servers, and governance for real-time, secure, production-scale AI consumption. Guide senior engineers and partner with Applied AI, Product, Architecture, and Security.

Lead strategy, architecture, and delivery of a production-scale data platform enabling agentic AI and analytics. Architect retrieval pipelines, vector stores, feature and knowledge-graph integrations, lakehouse and streaming (Databricks, Spark, Kafka) on AWS. Ensure governance, lineage, data quality, observability and AI-specific controls. Partner with Applied AI and Product, set standards for data products exposed to agents, and guide a team of senior data engineers while remaining hands-on for architectural decisions.