Top Tech Jobs & Startup Jobs in Dublin

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.

Design and manage public and on-prem cloud infrastructure, build and optimize CI/CD pipelines, champion Infrastructure as Code, drive monitoring and reliability, collaborate with R&D to troubleshoot and scale a high-volume, low-latency decisioning platform, and lead DevOps automation and cost-optimization efforts.

Lead global Quality Engineering for CNPF products and platforms. Define and execute QE strategy across full-stack, data/ML, and infrastructure; drive test automation, shift-left/right practices, release gates, incident root-cause analysis, tooling decisions, budgeting, and talent development.

Lead design and delivery of secure, scalable agentic AI applications and cloud-native services. Drive architecture, implementation, observability, CI/CD, and governance; mentor engineers and collaborate with product, AI, security, and platform teams to move AI solutions from prototype to production.

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. 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 and scale a global Quality Engineering organization across full-stack applications and data/ML platforms. Define QE strategy, drive test automation, embed shift-left/right practices, establish AI/ML evaluation frameworks, own release quality gates, manage budget/vendor relationships, and hire and develop QE leadership.

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 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.