Top Tech Jobs & Startup Jobs in Dublin

Develop, test, and deploy AI/ML solutions including transformer-based and generative models. Build data preparation, feature engineering, training pipelines, and model-serving solutions. Support evaluation, monitoring, integration into applications/APIs, and follow MLOps and software engineering best practices while collaborating with cross-functional teams.

Lead day-to-day administration, monitoring, patching, and troubleshooting of enterprise load balancers and WAFs to ensure high availability. Develop automation, monitoring strategies, and certificate/PKI management. Collaborate with engineering and project teams to design scalable architectures, run cross-team troubleshooting, and provide training to operations staff.

Manage lifecycle, monitoring, patching, and stability of enterprise load balancer platforms. Troubleshoot complex network and application traffic issues, develop automation and monitoring, collaborate on architecture and projects, and train operations teams to ensure high availability and adherence to standards.

Lead daily administration, monitoring, patching and automation of enterprise load balancers and WAFs. Troubleshoot complex network and application traffic issues, design monitoring strategies, support build/deploy automation, and collaborate with engineering and delivery teams to ensure 99.999% platform availability and operational readiness.

Lead DevOps Engineer builds and operates scalable CI/CD pipelines, cloud infrastructure, and runtime environments for foundation model development and AI workloads. Responsibilities include infrastructure as code, container orchestration, model deployment (inference and batch), monitoring/alerting, security best practices, Databricks support, incident response, and collaboration with AI, data, and software engineers to automate and harden production AI systems.

Lead DevOps Engineer will build and operate cloud infrastructure and CI/CD pipelines to deploy and run foundation models and AI/data workloads. Responsibilities include infrastructure as code, container orchestration, monitoring, security best practices, Databricks support, incident response, and collaboration with AI, data, and software engineers to enable reliable, scalable production AI systems.

Build and operate scalable CI/CD pipelines, cloud infrastructure (primarily AWS), and runtime environments for foundation-model and data workloads. Automate deployments, manage IaC, container orchestration, Databricks integration, monitoring/observability, and secure pipelines. Collaborate with AI, data, and software engineers, troubleshoot production issues, and promote DevOps best practices for reliable model training and inference at scale.

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 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.

Lead a distributed agile team to design, develop, and maintain high-performance, high-volume Java applications for commercial virtual payments. Define features, contribute to system development, ensure SLAs, mentor engineers, improve processes, and deliver secure, scalable API/Batch/UI solutions.