Top Tech Jobs & Startup Jobs in Dublin

Lead enterprise-wide Oracle Fusion Cloud Finance and SCM architecture, governance, implementations, rollouts, and transformation initiatives. Define scalable solution frameworks, integrations, data migration and governance strategies, security, compliance, extensions, and reporting architecture. Partner with business leaders, IT teams, and vendors, conduct architecture reviews and workshops, guide technical and functional decisions, mentor delivery teams, and identify opportunities for automation and continuous improvement.

Lead the architecture, design, and delivery of enterprise-scale applications and solutions. Provide technical leadership across projects, align technology strategies with business objectives, drive AWS cloud adoption, optimize MySQL and NoSQL performance, guide React and JavaScript front-end architecture, establish CI/CD pipelines, and implement monitoring and observability. Collaborate with development, QA, DevOps, product, and business stakeholders while enforcing scalability, security, and architectural best practices.

Lead the design, development, and deployment of scalable backend services and React components in a headless architecture. Manage cloud solutions, CI/CD pipelines, Dockerized applications, and database integrations. Collaborate with stakeholders, translate business requirements into technical solutions, guide architectural decisions, lead technical discussions, and mentor junior developers. Preferred experience includes payment gateways, PIM and OMS platforms, DevOps tools, and Agile methodologies.

Lead the design, development, deployment, and monitoring of scalable multimodal AI and machine learning systems across computer vision, audio, and NLP. Own the complete ML lifecycle, build training pipelines and inference APIs, productionize models with cloud and MLOps infrastructure, implement retraining workflows, and apply emerging techniques such as foundation models, generative AI, and self-supervised learning. Collaborate cross-functionally with engineering, product, data, and design teams to deliver business impact.

Lead the architecture, development, and delivery of scalable frontend applications using modern JavaScript frameworks. Manage and mentor frontend engineers, establish UI standards, conduct code reviews, collaborate with stakeholders and backend teams, and ensure performance, accessibility, maintainability, and high-quality delivery. Support hiring, team scaling, CI/CD practices, and frontend project scaffolding while owning technical strategy and hands-on implementation.

Build and maintain scalable Python backend systems, REST APIs, and production-ready applications. Own features throughout planning, development, testing, deployment, monitoring, and troubleshooting. Collaborate with frontend, DevOps, and data teams, while working directly with clients to understand needs, explain trade-offs, and deliver reliable solutions. Apply testing, cloud, containerization, CI/CD, API security, debugging, and performance optimization practices.

Build and maintain scalable Python backend systems, REST APIs, and production-ready business logic. Own features across planning, development, testing, deployment, monitoring, and troubleshooting. Collaborate with frontend, DevOps, and data teams while working directly with clients to understand needs, explain trade-offs, and gather feedback. Develop reliable, readable, tested code using modern backend frameworks, databases, containers, cloud platforms, CI/CD pipelines, and API security practices.

Design, build, evaluate, and deploy production-grade AI and machine learning models for complex business challenges. Responsibilities include exploratory data analysis, feature engineering, statistical experimentation, advanced modeling across NLP, computer vision, forecasting, and recommendations, and adapting Transformers, LLMs, and graph neural networks. The role also involves model interpretability, responsible AI, MLOps collaboration, stakeholder communication, and integrating current research into scalable production systems.

Support AI and machine learning initiatives through data collection, cleaning, transformation, analysis, ETL pipeline development, feature engineering, basic model development, dashboards, reporting, documentation, and SQL and cloud learning under senior mentorship.

Build and optimize scalable batch and streaming data pipelines, data lakes, warehouses, and feature stores across AWS, GCP, and Azure. Enable end-to-end MLOps workflows including model training, deployment, monitoring, retraining, and versioning. Implement infrastructure automation, data governance, security, observability, and cost optimization while partnering with data scientists and ML engineers to productionize machine learning systems.