Top AI & Machine Learning Jobs in Dublin

Lead product strategy and delivery for Mastercard's Data & AI platform and applications. Define roadmap, data-as-product practices (SLAs, governance, ontologies), guide cross-functional teams, operationalize agentic AI and HITL workflows, ensure compliance, and engage stakeholders to build scalable, user-centric data products.

Own and evolve an AI-native data platform and applications: define roadmap, treat data as product, establish SLAs and governance, lead cross-functional teams, operationalize agentic AI and HITL workflows, ensure compliance, and translate regional needs into scalable data products.

Lead product strategy and execution for Mastercard's Data & AI platform. Define roadmaps, data-as-product SLAs, ontologies, and reusable datasets. Drive cross-functional development, ensure compliance and security, operationalize agentic AI and HITL workflows, and align regional requirements to deliver scalable, user-centric data products.

Lead engineering teams to build and operate cloud-native API platforms exposing foundation model capabilities. Own API and service architecture, security, scalability, observability, CI/CD, and integration with AI teams while driving delivery, hiring, and stakeholder alignment.

Lead engineering team building and operating cloud-native API platforms exposing foundation model capabilities. Drive API and service architecture, security, reliability, CI/CD, observability, and integration of AI models into production while hiring, coaching, and managing multi-stream delivery aligned to product and business priorities.

Design and deliver production-grade AI solutions using LLMs, RAG, and vector search. Build knowledge bases, APIs, and microservices on cloud platforms (AWS, Cloudera, Databricks). Use frameworks like LangChain and MCP, ensure security/compliance, monitor model performance, and mentor junior engineers while collaborating across product, engineering, and data teams.

Lead hands-on engineering for agentic AI products and platforms: define architecture and vision, build scalable agent frameworks, partner with Product/Data/Security, ensure production-grade reliability, observability, safety, and developer tooling to accelerate enterprise AI adoption.

Lead engineering for agentic AI products and platforms: define vision, build scalable agent frameworks, deliver production-grade LLM-powered systems, drive AI tooling adoption, ensure security/governance, and partner cross-functionally to operationalize agentic capabilities.

Design and deliver production-grade AI solutions using LLMs, RAG, and vector-search knowledge bases. Build APIs and microservices, integrate with cloud and data platforms (AWS, Cloudera, Databricks), apply prompt engineering and orchestration frameworks, monitor model performance, ensure security/compliance, and mentor engineers.

Lead design and deliver enterprise-grade AI solutions using LLMs, RAG, LangChain and vector search. Build and maintain knowledge bases, architect scalable cloud solutions (AWS, Cloudera, Databricks), develop APIs/microservices, ensure security/compliance, monitor model performance, mentor engineers, and collaborate cross-functionally to translate business needs into production AI systems.