Top Tech Jobs & Startup Jobs in Dublin

Lead the architecture and hands-on development of secure, scalable Open Banking services. Design microservices and cloud-based systems, participate in coding and architecture reviews, guide technical initiatives, mentor engineers, influence product and business decisions, and drive enterprise architectural alignment. Apply secure development practices, cryptography, compliance knowledge, and vulnerability management to deliver resilient financial services platforms.

Own end-to-end quality engineering for agentic applications, intelligent workflows, APIs, user interfaces, and platform services. Design automation frameworks, test AI and distributed systems, establish CI/CD quality gates, and evaluate performance, reliability, resilience, observability, and scalability. Collaborate with engineering, product, architecture, and AI teams; conduct root-cause analysis; improve testability and release confidence; and mentor team members on automation and quality practices.

Design and deliver Java-based capabilities for Mastercard’s large-scale Decision Enrichments platform. Build secure, resilient distributed systems, REST APIs, microservices, and event-driven applications. Collaborate with product, architecture, quality, and engineering teams throughout design, development, testing, deployment, and operations. Troubleshoot production issues, improve platform architecture, apply automation and DevOps practices, document technical designs, and use AI-assisted engineering across the software development lifecycle.

Own end-to-end quality engineering for AI and agentic applications, APIs, user interfaces, distributed systems, and platform services. Design automation frameworks, define AI testing strategies, implement CI/CD quality gates, assess performance and resilience, identify defects, drive root-cause resolution, and mentor engineers. Collaborate with product, engineering, architecture, and AI teams to improve testability, release confidence, and engineering effectiveness.

Designs and delivers Java-based capabilities for Mastercard’s large-scale Decision Enrichments platform. Builds secure, resilient distributed systems, RESTful microservices, and event-driven applications; contributes to architecture, testing, deployment, observability, and operational excellence. Collaborates with product, architecture, quality, and engineering teams, troubleshoots production issues, improves platform capabilities, and applies AI-assisted engineering practices. The role requires hands-on development, technical leadership, enterprise-scale engineering, and continuous improvement.

Own end-to-end quality engineering for AI and agentic applications, APIs, user interfaces, distributed systems, and platform services. Design automation frameworks, define AI testing strategies, integrate quality gates into CI/CD pipelines, assess performance and reliability, investigate defects, and drive root-cause resolution. Collaborate with engineering, product, architecture, and AI teams while mentoring colleagues and improving testability, release confidence, and engineering effectiveness.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps practices, automation, CI/CD workflows, and monitoring frameworks; address performance issues, data drift, and operational incidents. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance groups to define success metrics, meet regulatory and ethical standards, and deliver business value. Lead cross-functional initiatives and influence outcomes without direct authority.

Leads end-to-end deployment, operationalization, monitoring, and continuous improvement of AI/ML solutions in production. Establishes MLOps automation and workflows, manages model performance and data drift, responds to incidents, and conducts root cause analysis. Partners with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical requirements, and ensure AI deployments deliver business value.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps automation and CI/CD workflows, manage incidents and root-cause analysis, and ensure model reliability, scalability, governance, compliance, and ethical operation. Partner with AI engineers, product teams, business stakeholders, and risk functions to define success metrics, translate business needs into technical requirements, and maximize the business value of AI solutions.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps practices, automation, reliability monitoring, and incident response processes. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical standards, resolve operational issues, and demonstrate business value.