Top Engineering Jobs in Dublin

Lead and scale Quality Engineering across full-stack and data/ML platforms. Define QE strategy, drive test automation, shift-left/right practices, establish AI/ML and data quality frameworks, own release quality gates, perform root-cause analysis, manage budget/vendors, and hire and develop global QE talent to ensure product reliability and customer experience.

Lead data engineering strategy and delivery for CNPF, building lakehouse, streaming, retrieval, vector/feature stores and MCP servers to support agentic AI, LLMs, and analytics. Ensure governance, observability, security, and real-time data access while guiding senior engineers and partnering with Applied AI, Product, and Architecture.

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.

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.

Lead strategy and delivery of the data platform powering analytics and agentic AI across business verticals. Architect lakehouse, streaming, retrieval, vector stores, feature stores, 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, and guide a team of senior data engineers while staying hands-on to unblock technical challenges.

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.

Lead and scale Quality Engineering across full-stack and data/ML products. Define QE strategy, drive test automation, shift-left/right practices, release gates, root-cause analysis, and AI/ML guardrail testing. Manage budget, vendors, hiring, and career paths while improving product reliability and customer experience.

The Program Manager will support the Data Security Products Group, overseeing project scheduling, scoping, and execution while collaborating with engineering and product management teams to ensure timely delivery and resolution of issues.

Lead a small engineering team to define and execute internationalization and translation platform strategy. Build tooling, standards, metrics, and operational processes to drive i18n adoption across many repos. Partner with product, apply AI to streamline translation workflows, and mentor engineers while balancing experimentation and platform stability.

Lead engineering for agentic AI products and platforms: define vision, build scalable agent frameworks, deliver production-grade LLM-powered systems, drive AI tooling adoption, ensure security/governance, and partner cross-functionally to operationalize agentic capabilities.

Lead and deliver production-grade agentic AI systems and platforms. Define engineering vision, build and scale teams, architect agent frameworks and orchestration, ensure security, observability, and governance, partner with Product and Data Science, and drive adoption of AI tooling and developer productivity across the organization.

Lead hands-on engineering for agentic AI products and platforms: define architecture and vision, build scalable agent frameworks, partner with Product/Data/Security, ensure production-grade reliability, observability, safety, and developer tooling to accelerate enterprise AI adoption.

Lead engineering strategy and hands-on delivery for agentic AI products and platforms. Build and scale teams and reusable agent frameworks, drive architecture for production-grade LLM/agent systems (context, memory, tool integration, orchestration, observability, safety), partner with Product and Data Science, and establish engineering standards, tooling, and measurable success criteria to move experiments into governed, scalable production.

Lead engineering strategy and delivery for agentic AI products and platforms. Build and scale teams, drive architecture for production-grade agent systems, establish reusable frameworks, partner cross-functionally, and ensure security, observability, and governed AI adoption.

Lead engineering strategy and hands-on delivery for agentic AI products and reusable agent frameworks. Build and scale teams and cloud-native platforms, drive architecture for production-grade agent systems (memory, orchestration, tool integration, observability, safety), partner cross-functionally, establish engineering frameworks, and measure adoption, quality, and business impact.

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 engineering strategy and hands-on delivery for agentic, LLM-powered systems and reusable AI platforms. Build and scale teams and frameworks for orchestration, retrieval, observability, safety, and production-grade deployment; partner with Product, Data Science, Security, and Platform teams to move agentic solutions from experimentation to governed, scalable production.

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.

Lead and scale engineering teams to design, build, and productionize agentic AI systems and reusable platform capabilities. Define architecture, frameworks, observability, safety guardrails, and developer tooling to move LLM/agent solutions from experimentation to secure, scalable enterprise production while partnering with product, data science, security, and platform teams.

Lead engineering for production-grade agentic AI products and reusable agent frameworks. Drive architecture, platform capabilities (lifecycle, retrieval, observability, safety), team scaling, AI tooling adoption, and cross-functional delivery to turn experiments into secure, scalable enterprise solutions.

The Senior Solutions Engineer engages with customers to define their needs, delivers pre-sales experiences, designs proof of concepts, and collaborates with product and engineering teams to ensure customer success and feedback integration.

Lead a team developing and deploying foundation models and generative AI solutions. Drive end-to-end delivery from problem definition to production, collaborate with product, data engineering, and platform teams, establish evaluation and responsible AI practices, and provide technical leadership and coaching to ensure robust, scalable, measurable AI use cases.

Lead engineering team building and operating API platforms and backend services to expose foundation model capabilities. Own architecture, security, scalability, observability, CI/CD, and reliability. Partner with AI teams to integrate models, manage multiple workstreams, hire and develop talent, and translate product needs into technical roadmaps.

Lead engineering teams to build and operate cloud-native API platforms exposing foundation model capabilities. Own API and service architecture, security, scalability, observability, CI/CD, and integration with AI teams while driving delivery, hiring, and stakeholder alignment.

Lead engineering team building and operating cloud-native API platforms exposing foundation model capabilities. Drive API and service architecture, security, reliability, CI/CD, observability, and integration of AI models into production while hiring, coaching, and managing multi-stream delivery aligned to product and business priorities.