Top DevOps Jobs in Dublin

Monitor and respond to production alerts, troubleshoot distributed applications, improve reliability and system health, support CI/CD pipelines and deployments, automate operational processes, conduct incident response and blameless postmortems, and collaborate with development, operations, and product teams on DevOps best practices.

Provides senior individual-contributor technical leadership for complex, enterprise-scale software engineering initiatives. Designs and delivers secure, scalable, resilient systems; drives architecture, development, testing, deployment, automation, observability, and operational excellence. Partners with product, business, architecture, and engineering stakeholders, resolves complex production issues, reduces technical debt, improves engineering productivity, and influences technical strategy across teams and geographies.

Provides senior individual-contributor technical leadership for complex, enterprise-scale software systems. Leads architecture, design, development, testing, deployment, and operational excellence across distributed platforms. Partners with product, business, architecture, and engineering teams to translate strategic requirements into secure, scalable, resilient solutions. Drives automation, reliability, performance, technical-roadmap decisions, engineering standards, and technical-debt reduction while mentoring engineers and influencing cross-functional stakeholders.

Lead the design and evolution of enterprise data platforms and pipelines supporting credit risk, decisioning, regulatory reporting, analytics, and AI initiatives. Architect cloud-based ETL and ELT solutions using Databricks, Spark, Delta Lake, and related technologies. Establish data quality, governance, lineage, observability, security, and compliance controls. Drive modernization, performance optimization, technical standards, cross-functional delivery, architectural decisions, mentoring, talent assessment, and strategic roadmap planning.

Provides expert individual-contributor leadership for enterprise-scale software engineering. Designs and delivers secure, resilient, scalable solutions across services and platforms; drives architecture, development, testing, deployment, automation, observability, and operational readiness. Partners with product, business, architecture, and engineering stakeholders, resolves complex production issues, reduces technical debt, advances engineering standards, and influences technology roadmaps. The role also mentors engineers and contributes to architecture forums, technical communities, and enterprise-wide initiatives.

Provides senior technical leadership as an individual contributor, designing and delivering secure, scalable, resilient enterprise software systems. Leads architecture, development, testing, deployment, operational readiness, automation, and production issue resolution. Partners with product, business, architecture, and engineering teams to shape technical strategy, improve engineering standards, reduce technical debt, and influence technology roadmaps across global teams.

Build and maintain scalable ETL pipelines, integrate data from databases, APIs, streaming platforms, and external providers, and transform large datasets for analytics and machine learning. Establish reusable engineering frameworks, data governance, monitoring, alerting, and reliability practices. Collaborate with analytics and business teams on cloud data migration, predictive model enablement, and healthcare cost reduction. Required skills include SQL, Python, Scala or Java, cloud platforms, Snowflake or Databricks, DevOps, Git, and CI/CD.

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, testing, deployment, and operations. Troubleshoot production issues, improve platform architecture, apply engineering best practices, and use AI-assisted development practices. Contribute technical leadership, documentation, automation, observability, and continuous improvement for mission-critical payment decisioning systems.

Design and deliver Java-based capabilities for Mastercard’s large-scale decisioning platform. Build secure, resilient distributed systems, REST APIs, microservices, and event-driven solutions. Collaborate with product, architecture, business analysis, and quality teams throughout design, development, testing, deployment, and operations. Troubleshoot production issues, improve platform architecture, apply engineering best practices, and use AI-assisted development practices. Provide technical leadership while supporting continuous improvement and operational excellence.

Design and deliver Java-based capabilities for Mastercard’s large-scale Decision Enrichments platform. Build secure, resilient distributed systems, REST APIs, and event-driven applications; collaborate with product, architecture, and quality teams; contribute to design, testing, deployment, documentation, and operational excellence. Troubleshoot production issues, improve engineering practices, evaluate technologies, and apply AI-assisted development throughout the software lifecycle.

Design and deliver Java-based capabilities for Mastercard’s high-scale Decision Enrichments platform. Build secure, resilient distributed systems and RESTful microservices, collaborate with cross-functional Agile teams, contribute to architecture and testing, troubleshoot production issues, improve automation and engineering practices, and apply AI-assisted development throughout the software lifecycle.

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 solutions. Collaborate with product, architecture, quality, and engineering teams across the software lifecycle, including testing, deployment, operations, and troubleshooting. Contribute to technical design, automation, continuous improvement, observability, and engineering standards while applying AI-assisted development 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 cross-functional Agile teams, contribute to architecture and technical designs, ensure quality through testing and CI/CD, troubleshoot production issues, and drive automation and engineering improvements. Apply AI-assisted development practices while supporting high-performance, mission-critical payment decisioning systems.

Design and deliver Java-based capabilities for Mastercard’s scalable Decision Enrichments platform. Build resilient distributed systems, REST APIs, microservices, and event-driven applications; contribute across architecture, implementation, testing, deployment, and operations. Collaborate with product, architecture, quality, and engineering teams, troubleshoot production issues, improve automation and engineering practices, and produce technical documentation. Apply AI-assisted development practices while helping shape secure, high-performance decisioning platforms for fraud prevention, risk management, and payment intelligence.

Designs and delivers Java-based capabilities for Mastercard’s large-scale Decision Enrichments platform. Builds resilient, secure distributed systems, APIs, microservices, and event-driven solutions; contributes across architecture, implementation, testing, deployment, and operations. Collaborates with product, architecture, quality, and engineering teams; troubleshoots production issues, improves platform capabilities, documents technical designs, and applies AI-assisted engineering practices. The role supports real-time decisioning for fraud prevention, risk management, payment intelligence, and customer experiences.

Design and deliver Java-based capabilities for Mastercard’s Decision Enrichments platform. Build scalable, resilient distributed systems, REST APIs, microservices, and event-driven applications. Collaborate with product, architecture, quality, and engineering teams throughout design, testing, deployment, and operations. Troubleshoot production issues, improve platform architecture, apply engineering best practices, and use AI-assisted development practices. The role requires technical leadership, enterprise-scale software expertise, and a focus on secure, high-performance payment decisioning systems.

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.

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.

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 solutions in production. Establish MLOps automation and workflows, manage model performance and data drift, respond to incidents, and conduct root cause analysis. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical requirements, and ensure AI solutions 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.

Leads deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establishes MLOps practices, automation, reliability frameworks, and incident response processes. Partners with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical requirements, resolve performance or data-drift issues, and ensure AI solutions deliver business value.

Leads the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establishes MLOps automation and CI/CD workflows, monitors 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 solutions deliver business value.