Top Engineering Jobs in Dublin

Design, build, and deploy transformer-based and generative AI models into production. Develop training workflows, data pipelines, model serving/inference, and MLOps practices. Collaborate with product, data, and engineering partners, perform performance tuning, and ensure model reliability, monitoring, and lifecycle management.

Design, build, and deploy transformer-based and generative AI models into production. Develop data pipelines, training workflows, model serving and inference, apply MLOps practices, monitor performance, collaborate with product and engineering teams, and support troubleshooting and optimization.

Design, build, and deploy transformer-based and generative AI models into production. Develop training workflows, data pipelines, feature engineering, model serving, and MLOps (tracking, versioning, testing, monitoring). Collaborate with product, data, and engineering teams, perform model tuning and troubleshooting, and follow security and engineering standards.

Design, build, and deploy transformer-based and generative AI models into production. Develop data pipelines, feature engineering, training workflows, and model serving. Apply MLOps practices, collaborate with product and engineering teams, and support production troubleshooting and performance tuning.

Design, build, and deploy transformer-based and generative AI models into production. Develop data pipelines, feature engineering, training workflows, model serving, and MLOps (tracking, versioning, testing, monitoring). Collaborate with product, data, and engineering teams, perform code reviews, and troubleshoot and optimize production model performance.

Design, build, and deploy transformer-based and generative AI models into production. Develop data pipelines, feature engineering, training workflows, model serving, and MLOps practices. Collaborate with product and engineering teams, perform code reviews, monitor model performance, and support production troubleshooting and optimization.

Design, develop, and deploy transformer-based and generative AI models into production. Build data pipelines, feature engineering, and training workflows. Implement model serving, inference, monitoring, and MLOps practices. Collaborate with product, data, and engineering teams, participate in code reviews, troubleshoot production model performance, and continuously improve ML engineering practices.

Design, build, and deploy transformer-based and generative AI models into production. Develop training workflows, data pipelines, feature engineering, model serving, and MLOps practices. Collaborate with product, data, and engineering teams to operationalize and monitor model performance.

Design, build, and deploy transformer-based and generative AI models into production. Develop training and inference pipelines, implement model serving and MLOps practices, collaborate with product and data teams, and troubleshoot and optimize model performance in production environments.

Design, build, and productionize transformer-based and generative AI models. Develop training workflows, data pipelines, feature engineering, model serving, and inference APIs. Apply MLOps best practices (experiment tracking, versioning, testing, monitoring). Collaborate with product, data, and engineering teams to translate requirements, perform performance tuning, and maintain production model reliability.

Design, build, and deploy transformer-based and generative AI models into production. Implement training pipelines, feature engineering, model serving, MLOps practices, monitoring, and integration with downstream APIs. Collaborate with product, data, and engineering teams to translate requirements and troubleshoot production model performance.

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.

Lead development and deployment of scalable AI/ML solutions. Translate stakeholder needs into technical designs, oversee data preparation, model development, deployment, performance tuning, and mentor engineers and data scientists.

Design, build, and productionize transformer-based and generative AI models. Develop training and inference pipelines, integrate models into APIs, apply MLOps practices, monitor performance, and collaborate with product and engineering teams to deliver reliable AI solutions.

Design, build, and deploy transformer-based and generative AI models into production. Implement data pipelines, training workflows, model serving, and MLOps practices. Collaborate with product, data, and engineering teams, participate in code reviews, and troubleshoot model performance in production while advancing technical skills.

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.

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 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 transformer-based and generative AI models into production. Implement training pipelines, feature engineering, model serving, MLOps practices, monitoring, and collaborate with product, data, and engineering teams to ensure reliable, performant AI systems.

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 and deliver end-to-end AI/ML solutions: design, build, deploy, and tune models; integrate and prepare data; translate stakeholder needs to technical solutions; mentor and review junior engineers; manage project timelines, budgets, and post-implementation reviews.

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.

Lead AI Engineer to design, develop, and deploy scalable AI/ML solutions. Lead projects from concept to production, supervise data integration and model performance, mentor engineers, and present outcomes to stakeholders. Work includes advanced ML, deep learning, NLP, generative AI, and MLOps.

Lead development and deployment of scalable AI/ML solutions, translate stakeholder needs into technical designs, oversee data integration and model performance, mentor engineers, and deliver production AI systems and visualizations on time and on budget.

Design, build, and deploy transformer-based and generative AI models into production. Develop data pipelines, training workflows, model serving/inference, and MLOps practices. Collaborate with product, data, and engineering teams to integrate models into applications, run evaluations, troubleshooting, and performance tuning while following engineering standards.