Top Engineering Jobs in Dublin

Lead AI initiatives: architect and deploy scalable ML systems, translate stakeholder needs into AI solutions, oversee data integration and model performance, deliver AI-driven insights, and mentor engineers and data scientists.

Lead design, development, and deployment of scalable AI/ML solutions. Translate stakeholder needs into models, oversee data preparation, mentor engineers, deliver production systems, and conduct performance reviews.

Lead development and delivery of scalable AI/ML solutions, translate stakeholder needs into technical designs, oversee data integration and model deployment, mentor and review junior engineers, and manage performance and post-implementation reviews.

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.

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.

Lead development and deployment of scalable AI/ML solutions; architect models and data pipelines; mentor engineers and data scientists; translate stakeholder needs to technical designs; ensure model performance, integration, and post-implementation reviews.

Lead development and deployment of advanced AI/ML solutions. Architect scalable ML systems, mentor engineers, translate stakeholder needs into technical designs, oversee data preparation, model deployment, performance tuning, and deliver measurable business impact.

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 preparation, model development, deployment, performance tuning, and mentor engineers and data scientists.

The Lead AI Engineer will develop advanced AI solutions, mentor a team, and oversee machine learning initiatives, ensuring data readiness and translating stakeholder needs into AI-driven solutions.

Lead creation of an AI-first engineering capability for database products: design reusable frameworks, ensure secure production code, guide AI-enabled delivery, set governance and guardrails, influence senior stakeholders, and mentor engineers to scale cross-product adoption.

The Lead Engineer will develop and operate security services in Rust, ensuring database security and performance while collaborating across teams to enhance MongoDB's security capabilities.

Lead and build a team of Site Reliability Engineers for MongoDB's Storage Layer Services, focusing on distributed storage systems and operational reliability.

Design, build, and deploy transformer-based and generative AI models into production. Develop training workflows, data pipelines, feature engineering, model serving, and inference APIs. Apply MLOps practices (tracking, versioning, testing, monitoring), collaborate with product and engineering teams, troubleshoot and optimize production models, 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, 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. 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 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, build, and deploy transformer-based and generative AI models for production; implement training and inference workflows, data pipelines, model serving, and MLOps practices; collaborate with product, data, and engineering partners and support production troubleshooting and performance tuning.

Design, build, and deploy transformer and generative AI models into production. Develop data pipelines, feature engineering, training and inference workflows. Implement model serving, monitoring, and MLOps practices. Collaborate with product, data, and engineering teams, participate in code reviews, troubleshoot and tune models, and ensure production-grade performance and reliability.

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.

Design, build, and deploy transformer-based and generative AI models into production. Develop training pipelines, feature engineering, model serving, and MLOps practices. Collaborate with product, data, and engineering teams, perform model evaluation and performance tuning, and maintain production monitoring and lifecycle management.

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 operationalize transformer-based and generative AI models; develop training and inference workflows, data pipelines, and MLOps practices; deploy and monitor models in production; collaborate with product and engineering teams.

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, 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.