Top AI Jobs in Dublin

Design, build, and deploy production AI/ML solutions including transformer and generative models. Implement data pipelines, feature engineering, training workflows, model serving, and MLOps practices. Collaborate with product and engineering teams, participate in code reviews, troubleshoot and optimize models in production.

Design, build, and deploy production AI/ML solutions, focusing on transformer and generative models. Develop data pipelines, training workflows, model serving, and MLOps practices. Collaborate with product, data, and engineering teams to ensure reliable, monitored, high-quality model performance.

Design, build, and deploy production-quality AI solutions. Develop and optimize transformer and generative models, manage data pipelines and training workflows, implement model serving and MLOps practices, and collaborate with product and engineering teams to operationalize and monitor AI systems.

Design, build, and deploy production AI solutions including transformer and generative models; develop data pipelines and feature engineering; implement model serving, monitoring, and MLOps practices; collaborate with product and engineering teams to ensure reliable, performant systems.

Design, build, and deploy transformer-based and generative AI models into production. Build data pipelines and feature engineering, implement model serving and inference, apply MLOps practices (tracking, versioning, testing, monitoring), collaborate with product and engineering teams, and troubleshoot and optimize model performance in production.

Design, build, and deploy production AI/ML solutions including transformer and generative models. Develop data pipelines, training workflows, model serving, and MLOps practices. Collaborate with product and engineering teams, perform evaluation and performance tuning, and ensure maintainable, production-grade AI systems.

Design, build, and deploy production AI solutions including transformer and generative models. Develop data pipelines, feature engineering, training workflows, model serving, and MLOps practices. Collaborate with product and engineering teams, participate in code reviews, and monitor/model performance in production.

Design, build, and deploy production AI/ML solutions including transformer and generative models. Develop data pipelines, feature engineering, training workflows, model serving, inference, and MLOps practices. Collaborate with product and engineering teams, perform code reviews, monitor and tune models in production.

Design, build, and productionize AI/ML solutions (including transformer-based and generative models). Develop data pipelines, feature engineering, training workflows, model serving/inference, and MLOps practices. Collaborate with product, data, and engineering teams, perform code reviews, troubleshoot and optimize models in production, and ensure model monitoring, versioning, and quality.

Design, build, and deploy production AI solutions including transformer-based and generative models. Develop data pipelines, feature engineering, training workflows, and model serving. Apply MLOps best practices, collaborate with product and engineering teams, support production troubleshooting and performance tuning, and participate in code reviews and technical planning.

The Principal Engineer will lead the design and delivery of core platform capabilities, focusing on scalable systems, AI application, and mentoring engineers while driving architectural decisions and improving operational systems.

As a Sr. Applied AI Engineer at Zapier, you will build and enhance AI platform capabilities, focusing on LLM Ops and ML Ops to support scalable AI development across teams.

The Product Manager for AI/ML Solutions will manage product lifecycle, work in Agile teams, and improve business processes using AI/ML technologies.

The Senior Software Engineer will lead application modernization initiatives, collaborate with cross-functional teams, develop tools, and provide strategic insights to improve customer engagement and product roadmap.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

The Senior Applied AI/ML Scientist will build and ship LLM-powered features, drive quality in evaluation, improve retrieval systems, and lead technical discussions with a focus on AI and engineering collaboration.

Design, build, and deploy production-grade agentic AI systems: multi-agent orchestration, RAG pipelines, provider abstraction, LLMOps (eval harnesses, prompt versioning, observability), cloud-native deployment (Kubernetes/Docker/CI-CD/IaC), and embed with client teams to deliver reusable patterns and measurable metrics for accuracy, latency, safety, and cost.

Lead large, complex customer deployments of Intercom and Fin, define scope and success metrics, manage timelines and stakeholders, perform hands-on platform configuration and integrations, coordinate technical readiness and data migrations, track adoption and outcomes, and evolve deployment methodology and best practices.

Lead SEO, content and AI discoverability strategy to drive organic traffic, conversions and brand authority. Conduct technical SEO audits, keyword research, content lifecycle and CMS management, backlink and digital PR strategies, AI-led search optimisation, cross-functional stakeholder alignment, and measure performance to support commercial targets.

Architect and build an AI-native automation and orchestration toolchain (fusing traditional tools with foundation models), enable and mentor early adopter teams, standardize practices across departments, drive infrastructure-as-code and CI/CD adoption, contribute to the IT/Sec operating system, and champion cross-departmental adoption of automation and AI platforms.

Evaluate AI-generated coding interactions end-to-end, judging usefulness, high-level correctness, engineering judgment, and quality of explanations. Provide clear, opinionated feedback, distinguish response quality levels, and help define best practices for AI-assisted developer workflows.

Oversee a portfolio of commercial accounts, ensure risk management, prepare credit analyses, monitor accounts, and liaise with clients including Fortune 500 executives.

Design, build and validate high-value AI proofs of concept and prototypes, focusing on practical business value, production-readiness and responsible AI. Collaborate with product, engineering, governance and stakeholders to run RAG, embeddings, prompt engineering and model evaluation, then recommend pilots or productionisation while ensuring security, observability, integration and operational feasibility.

Design AI behaviors, workflows, and user experiences, ensuring systems function reliably and integrate into user workflows. Collaborate with teams to define AI capabilities and evaluate system performance.

Support planning, intake, prioritisation, tracking and governance of the Enterprise AI portfolio. Gather and document business requirements, produce reports and dashboards, coordinate cross-functional delivery, track risks/issues, and improve portfolio management practices.