Top AI Jobs in Dublin

Lead product strategy and roadmap for a cloud-native enterprise AI/data platform enabling a federated data mesh. Own capabilities (ingestion, lakehouse, orchestration, observability, FinOps), enable domain teams, ensure governance/compliance, and drive cross-functional delivery, adoption, and executive stakeholder alignment.

Lead architecture, build, and operate production-grade AI systems at scale. Partner with product and data teams to translate business needs into resilient, cost-efficient AI platforms. Provide technical leadership, mentorship, MLOps best practices, and drive model lifecycle, deployment, monitoring, and inference optimization.

Lead a team building and deploying foundation models and generative AI solutions. Drive end-to-end delivery from problem definition to production, partner with stakeholders, ensure data readiness and MLOps, establish evaluation and responsible AI practices, and coach engineers to deliver reliable, scalable AI use cases.

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.

Define and deliver product strategy and roadmap for an enterprise cloud-native AI platform enabling a federated data mesh. Own capabilities like data ingestion, lakehouse storage, orchestration, observability, and FinOps. Enable domain teams as data product owners, embed federated governance, partner across Engineering, Architecture, Security, and Finance, and drive enterprise-scale delivery, adoption, and measurable outcomes.

Lead hands-on design, build, and production deployment of AI and agentic systems. Implement and operate ML services, pipelines, model serving, monitoring, evaluation, and retraining. Partner with data scientists, platform, security, and infrastructure teams to productionise models, ensure reliability, security, governance, and mentor engineers to raise technical standards.

Design, build, and operate production-grade AI systems and platforms; define technical direction and architecture; drive MLOps, deployment, monitoring, and lifecycle management; optimize inference and data pipelines; mentor senior engineers and influence cross-team AI strategy to deliver scalable, secure, production impact.

Lead hands-on development and production deployment of AI and agentic systems. Build and operate ML services, pipelines, model serving, monitoring, evaluation, and retraining. Partner with data science and platform teams to productionize models, ensure reliability, security, and governance, drive technical design, and mentor engineers.

Lead product strategy and roadmap for an enterprise, cloud-native AI/data platform enabling a federated data mesh. Own capabilities like ingestion, lakehouse storage, orchestration, observability, FinOps, and governance to empower domain teams, ensure enterprise-grade security/compliance, and drive adoption, scalability, and measurable business outcomes.

Lead product strategy, roadmap, and delivery for a cloud-native enterprise AI/data platform enabling a federated data mesh. Own platform capabilities (ingestion, lakehouse storage, orchestration, observability, FinOps), enable domain teams as data product owners, ensure governance/compliance, and drive cross-functional alignment, adoption, and enterprise-scale AI/ML outcomes.

Lead product strategy and roadmap for an enterprise, cloud-native AI/data platform enabling a federated data mesh. Own capabilities like ingestion, lakehouse storage, orchestration, observability, SLOs and FinOps. Partner with Engineering, Architecture, Governance, and business stakeholders to enable self-service data products, ensure compliance and reliability, drive adoption, and measure outcomes.

Lead an AI engineering team to develop, fine-tune, and deploy foundation models and generative AI for high-impact use cases. Drive end-to-end delivery, collaborate with product, data engineering and platform teams, embed responsible AI practices, and provide technical guidance to ensure scalable, production-ready solutions.

Lead hands-on development and production deployment of AI and agentic systems. Build and operate ML services, pipelines, and APIs; productionize models, implement serving/monitoring/retraining, collaborate with data science and platform teams, and ensure security, reliability, and governance at scale while mentoring engineers.

Lead hands-on development and production deployment of AI and agentic systems. Build and operate ML services, pipelines, and APIs; productionize models, implement serving, monitoring, evaluation, and retraining; collaborate with data science, platform, security, and infrastructure teams; lead technical design reviews and mentor engineers to ensure secure, reliable, scalable AI solutions.

Lead architecture, build, and operate production-grade AI systems at scale. Translate product requirements into robust, secure AI platforms, own technical decisions, mentor senior engineers and data scientists, and ensure reliable MLOps, inference optimization, and large-scale data processing.

Lead architecture, implementation, and operation of production-grade AI systems. Drive technical strategy, mentor senior engineers and data scientists, partner with product and data leaders, and ensure scalable, secure, and cost-efficient AI platforms spanning model development, deployment, inference optimization, and lifecycle management.

Lead architecture, development, and operation of production-grade AI systems. Define technical direction, build scalable cloud-native AI/ML platforms, enable MLOps, optimize inference, mentor senior engineers and data scientists, and translate product needs into robust AI solutions.

Lead a team of AI engineers to develop, fine-tune, and deploy foundation models and generative AI solutions. Drive end-to-end delivery of use cases, partner with product and data teams for scalable deployments, establish evaluation and responsible AI practices, and provide technical coaching and delivery oversight to meet program priorities.

Lead hands-on development and production deployment of AI and agentic systems. Build and operate ML services, pipelines, and model serving with monitoring, evaluation, and retraining; partner with data scientists and platform teams to ensure secure, reliable, and scalable AI solutions. Provide technical design leadership and mentorship.

Lead a team building and deploying foundation models and generative AI solutions. Drive end-to-end delivery from problem definition to production, partner with product and data teams, establish evaluation and governance practices, embed responsible AI, and provide technical leadership and coaching to ensure scalable, reliable, measurable AI use cases.

Lead product strategy and roadmap for an enterprise cloud-native AI platform enabling a federated data mesh. Own platform capabilities including ingestion, lakehouse storage, orchestration, observability, reliability, and FinOps. Enable domain teams to publish data products with embedded governance, partner cross-functionally to ensure compliance and scalability, define success metrics, drive adoption, and evangelize platform value to senior stakeholders.

Lead hands-on development and production deployment of AI and agentic systems. Build and operate ML services, pipelines, and APIs; implement model serving, monitoring, evaluation, and retraining; partner with data scientists to productionize models; drive technical design reviews, mentorship, and ensure performance, reliability, security, and governance at scale.

Lead product strategy and roadmap for an enterprise, cloud-native AI/data platform enabling a federated data mesh. Own capabilities like ingestion, lakehouse storage, orchestration, observability, and FinOps. Partner with engineering, governance, security, and business stakeholders to deliver scalable self-service data products, ensure compliance, and drive platform adoption and measurable outcomes.

Lead a team building and deploying foundation models and generative AI solutions. Drive end-to-end delivery from problem definition through fine-tuning, evaluation, integration, and production deployment. Partner with product, data engineering, and platform teams, embed responsible AI practices, set evaluation and experimentation standards, and provide technical leadership and coaching to ensure reliable, secure, measurable AI use cases.

Lead hands-on development and production deployment of AI and agentic systems. Build and operate ML services, model serving, monitoring, retraining, and pipelines. Partner with data scientists and platform teams to productionize models, ensure reliability, security, and governance, and mentor engineers.