Top Tech Jobs & Startup Jobs in Dublin

Lead hands-on design and delivery of secure, scalable agentic AI applications and cloud-native services. Define engineering patterns for production AI systems, drive end-to-end SDLC, improve developer workflows with AI, mentor engineers, and collaborate with product, AI, security, and platform teams to move prototypes to governed production.

Design, provision, and manage high-volume, low-latency cloud and on-prem infrastructure for a real-time decisioning platform. Build CI/CD pipelines, champion IaC, drive observability, cost optimization, troubleshoot distributed systems, and lead DevOps initiatives across global engineering teams.

Lead design and deliver production-grade agentic AI applications and cloud-native services. Define engineering patterns, ensure security, observability, and reliability, drive SDLC improvements, mentor engineers, collaborate with AI, product, and platform teams, and move prototypes to governed production.

Lead hands-on design and delivery of secure, scalable agentic AI applications and platform capabilities. Drive architecture, implementation, SDLC, observability, security, and cross-functional collaboration to take AI solutions from prototype to governed production. Mentor engineers and shape roadmaps to enable reliable, maintainable, and high-impact product delivery.

Lead strategy, architecture, and delivery of Mastercard's data platform for analytics and agentic AI. Design lakehouse, streaming, retrieval, vector/feature stores, and MCP servers. Ensure governance, lineage, observability, and AI-specific controls. Partner with Applied AI, Product, and Security; guide and grow a senior data engineering team while remaining hands-on to unblock technical challenges.

Design, provision, and operate high-volume, low-latency decisioning infrastructure across public and private clouds. Build and optimize CI/CD pipelines, implement Infrastructure as Code, drive observability and reliability, and collaborate with global engineering teams to support, troubleshoot, and scale the real-time Decision Management Platform.

Lead and scale a global Quality Engineering function across full-stack and data/ML systems. Define QE strategy, drive test automation, shift-left/right practices, establish AI and data quality frameworks, own release gates, perform RCA on incidents, manage budget and vendors, and recruit and develop QE leadership.

Lead design and deliver production-ready agentic AI applications and platform capabilities. Define engineering patterns, drive end-to-end SDLC, mentor engineers, ensure security, observability, reliability, cost controls, and collaborate with Product, Applied AI, Data Science, and stakeholders to move AI solutions from prototype to governed production.

Lead hands-on design and delivery of production AI/agentic applications and cloud-native services. Define engineering patterns for reliability, security, observability, and governance. Drive end-to-end delivery, mentor engineers, collaborate with Applied AI, Product, Security, and Platform teams, and shape technical roadmaps to scale AI-enabled products.

Lead global Quality Engineering across full-stack and data/ML platforms: define QE strategy, drive test automation, embed shift-left/right practices, own release quality gates, manage incidents, budgets, vendors, and build a high-performing QE organization.