Top Tech Jobs & Startup Jobs in Dublin

Build and lead a Support & Retention Experience function: monitor customer health, create post-production support with SLAs and escalations, design automated playbooks, align cross-functional data and tooling, surface expansion opportunities, report metrics to leadership, and hire and coach a high-performing team to improve retention and platform adoption.

Lead engineering for agentic AI products and platforms: build and scale teams, define architecture and frameworks for LLM-driven agents, ensure production-grade reliability, observability, security, and cross-functional adoption of AI tooling and governance.

Lead the architecture, design, and development of a cloud-native experimentation and analytics platform. Build scalable microservices, APIs, and data pipelines (Databricks/Spark), drive technical vision and modernization, ensure performance/security/reliability, mentor engineers, coordinate across global teams, and champion observability and continuous delivery practices.

Lead and manage a software engineering team building API-driven microservices for virtual card management. Oversee project delivery, architecture, code review, testing strategies, secure coding, performance engineering, cross-functional coordination, and team development including goal setting and performance appraisals.

Lead design and delivery of enterprise-grade AI solutions using LLMs, RAG, and vector search. Build knowledge bases, LLM-driven applications (LangChain), and scalable cloud architectures (AWS, Cloudera, Databricks). Implement secure microservices/APIs, monitor model performance in production, mentor engineers, and ensure compliance with data governance and security standards.

Lead engineering strategy and hands-on delivery for agentic AI and LLM-powered products. Build and scale platforms, reusable agent frameworks, and orchestration for production-grade agents. Partner with Product, Data Science, Security, and Platform teams to translate use cases into secure, observable, and reliable systems. Drive AI tooling adoption, engineering standards, lifecycle management, and developer experience while measuring adoption, quality, latency, resilience, and business impact.

Own and evolve Mastercard's Data & AI platform and products: define roadmaps, treat data as a product, lead cross-functional teams, ensure governance and compliance, operationalize agentic AI and HITL workflows, translate regional requirements, and deliver scalable, high-quality data products.

Build and lead a Support and Retention Experience function to monitor customer health, establish post-production support, automate playbooks, align cross-functional data, surface expansion opportunities, and provide leadership visibility to improve retention and platform adoption.

Own and evolve an AI-native data platform and applications: define roadmap and backlog, treat data as a product with SLAs and governance, lead cross-functional teams to build reusable data primitives, ontologies and datasets, operationalize agentic AI and HITL workflows, ensure compliance with security/privacy/regulatory requirements, and translate regional customer needs into scalable solutions.

Build and lead a Support & Retention Experience function to monitor customer health, establish post-production support and SLAs, automate playbooks, align cross-functional data, surface expansion opportunities, and drive retention and platform adoption.