Top AI Jobs in Dublin

Build, deploy, and operate production-grade AI and agentic systems. Implement model serving, monitoring, evaluation, retraining, pipelines and APIs. Partner with data scientists to productionize models, contribute to data preparation and feature engineering, participate in design reviews, and ensure solutions meet security, reliability, and governance standards.

Build and operate production-grade AI and agentic systems across the full lifecycle: design, model serving, pipelines, monitoring, retraining, and APIs. Partner with data scientists and platform teams to productionize models, ensure security, reliability, and governance, and participate in design reviews and knowledge sharing.

Design, build, and deploy production AI solutions including transformer-based and generative models. Build data pipelines, training workflows, model serving and inference, apply MLOps practices, collaborate with product and engineering teams, and monitor and tune models 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. 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 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 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.

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 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/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/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.

Lead product vision and roadmap for agentic AI solutions, own the backlog, translate business needs into user stories, partner with data scientists and engineers, manage Agile delivery cycles, ensure responsible AI practices, and monitor model performance and continuous improvement.

Lead scientific and strategic AI efforts for Vaccines Medical: define AI vision and roadmap, govern responsible AI standards, prioritize and deliver validated AI-enabled solutions, partner cross-functionally, oversee deployment and adoption, manage budget and vendors, and ensure regulatory, ethical, and scientific compliance across global medical affairs.

Build end-to-end full-stack AI features on a Ruby on Rails backend and React frontend, including LLM inference infrastructure, RAG, AI agents, and evaluation/observability. Design and run experiments (A/B tests), optimize systems for production performance and reliability, and iterate rapidly with product and design partners.

As a Principal Software Engineer, you will architect and lead AI Foundations projects, mentor engineers, and ensure quality standards across the AI platform.

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.

Senior technology leader to define and scale AI engineering and consulting platforms. Lead integration of GenAI, agent orchestration, knowledgebases, finops/tokenops, and platform commercialization to drive productivity for thousands of consultants across enterprise stakeholders.

Senior technology leader to define and scale AI engineering and consulting platforms. Partner with product, AI, data, and business teams to build agentic workflows, orchestration, knowledgebase capabilities, finops/tokenops, and reusable AI assets to enable productivity and commercialization across thousands of consultants.

Lead technology strategy and execution for AI-enabled consulting platforms. Build and scale GenAI, agent orchestration, knowledgebase, finops/tokenops, and productivity tooling; partner with product, data, security, and business teams to commercialize AI solutions and drive large-scale consulting transformation.

Lead technology strategy and execution for AI-enabled consulting platforms, scaling agentic workflows, commercial AI tooling, integrations, and reusable capabilities across thousands of consultants while partnering with enterprise AI, data, security, and business leaders.

Lead technology strategy and delivery to scale AI productivity and commercialize agent-enabled consulting platforms. Build and integrate GenAI tooling, agent orchestration, knowledgebases, cost/usage governance, and reusable agent libraries while partnering across product, data, security, and business leaders.

Lead technology strategy and execution to build scalable AI and agentic platforms for Consulting. Partner with product, AI, data, and enterprise teams to enable agent orchestration, knowledge bases, finops/tokenops, commercialization, and productivity tooling for thousands of advisors.

Lead technology strategy and delivery for enterprise consulting transformation, scaling GenAI and agentic platforms (Claude, Copilot, Cowork), enabling agent orchestration, AI knowledgebases, finops/tokenops, and reusable agent libraries. Partner cross-functionally with product, data, security, compliance, and business leaders to commercialize AI, drive productivity at scale, and build platform capabilities supporting thousands of consultants.