Top Machine Learning Jobs in Dublin

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

Lead and mentor a data science team building ML and LLM solutions for cybersecurity. Translate business needs into research and production ML objectives, prioritize roadmaps, collaborate cross-functionally, and drive technical vision, model development, and operationalization for malware detection and threat analysis.

Lead a multidisciplinary engineering team to design, build and deliver scalable AI and quantum-enabled payment products. Define requirements, drive project execution using agile and AI-assisted processes, mentor engineers, coordinate with data scientists, and represent the team internally and externally.

Lead a multidisciplinary engineering team building scalable Quantum and AI/ML-enabled payment products. Define requirements, drive agile delivery, research/design solutions, manage projects, coach talent, and engage with external partners and events.

Lead and manage an agile software engineering team building scalable AI and Quantum-infused payment solutions; define requirements, drive delivery, mentor talent, collaborate with data scientists and UI/UX, and represent the team externally.

Lead a multidisciplinary engineering team to design, build and scale AI and Quantum-enabled payment solutions. Drive agile delivery, define requirements, productionize Python/Jupyter solutions, deploy on Kubernetes, mentor talent, and engage with internal and external innovation partners.

Lead and manage a multidisciplinary engineering team building scalable products that combine Quantum and AI/ML technologies. Drive project delivery using agile and AI-assisted processes, define requirements, design scalable solutions, engage external partners, and mentor engineering leaders to bring innovations to market globally.

Lead and manage an agile software engineering team to design, build and scale innovative products that combine Quantum and AI/ML. Define requirements, drive project execution, collaborate with data scientists and engineers, and deploy solutions using Python, Jupyter and containerized virtual environments (including Kubernetes). Coach and grow talent, ensure compliance with standards, and represent the team externally.

Lead and manage a multidisciplinary engineering team to design, build and deliver scalable software products that combine Quantum and AI/ML technologies; define requirements, drive agile execution, mentor talent, and engage with internal and external partners to bring innovative payment solutions to market.

Lead and manage a software engineering team in Mastercard Foundry R&D focused on Quantum, AI/ML. Define requirements, design scalable solutions, drive agile project delivery, collaborate with data scientists and engineers, deploy Python/Jupyter-based systems to production (including Kubernetes), and coach talent while promoting innovation.

Serves as the primary Data and AI liaison for the Care Enablement business, aligning AI and analytics initiatives with strategic objectives. Identifies and prioritizes opportunities, develops roadmaps, builds business cases, tracks value, and translates business challenges into scalable technology solutions. Partners across product, R&D, engineering, manufacturing, quality, regulatory, supply chain, commercial, and digital teams. Promotes data-driven decision-making while ensuring regulatory and governance alignment, and leads cross-functional teams in a global medical technology environment.

Architects high-performance, real-time embedded software for datacenter power-management products. Responsibilities include gathering requirements, defining scalable architectures, selecting platforms and tools, conducting engineering trade-offs, documenting designs, communicating architecture decisions, improving development processes, and mentoring engineers. The role requires extensive full-lifecycle software development and embedded systems architecture experience, hardware schematic literacy, Agile and Git experience, and knowledge of signal-processing performance and latency trade-offs.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps practices, automation, CI/CD workflows, and monitoring frameworks; address performance issues, data drift, and operational incidents. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance groups to define success metrics, meet regulatory and ethical standards, and deliver business value. Lead cross-functional initiatives and influence outcomes without direct authority.

Leads end-to-end deployment, operationalization, monitoring, and continuous improvement of AI/ML solutions in production. Establishes MLOps automation and workflows, manages model performance and data drift, responds to incidents, and conducts root cause analysis. Partners with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical requirements, and ensure AI deployments deliver business value.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML solutions in production. Establish MLOps automation and workflows, manage model performance and data drift, respond to incidents, and conduct root cause analysis. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical requirements, and ensure AI solutions deliver business value.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps automation and CI/CD workflows, manage incidents and root-cause analysis, and ensure model reliability, scalability, governance, compliance, and ethical operation. Partner with AI engineers, product teams, business stakeholders, and risk functions to define success metrics, translate business needs into technical requirements, and maximize the business value of AI solutions.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps practices, automation, reliability monitoring, and incident response processes. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical standards, resolve operational issues, and demonstrate business value.

Leads deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establishes MLOps practices, automation, reliability frameworks, and incident response processes. Partners with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical requirements, resolve performance or data-drift issues, and ensure AI solutions deliver business value.

Leads the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establishes MLOps automation and CI/CD workflows, monitors performance and data drift, responds to incidents, and conducts root cause analysis. Partners with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical requirements, and ensure AI solutions deliver business value.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps automation and workflows, resolve performance bottlenecks and data drift, manage operational incidents, and conduct root cause analysis. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet business objectives, and ensure deployments satisfy regulatory, security, and ethical AI requirements.

Lead the deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps automation and CI/CD workflows, identify performance issues and data drift, manage incidents and root-cause analysis, and ensure reliable, scalable operations. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to define success metrics, meet regulatory and ethical requirements, and connect AI deployments to business objectives.

Leads the deployment, operationalization, monitoring, and continuous improvement of AI/ML solutions in production. Establishes MLOps automation and workflows, defines success metrics with stakeholders, manages model performance and data drift, ensures governance and compliance, and leads incident response and root-cause analysis. The role bridges technical and business teams while driving cross-functional initiatives and operational excellence.

Lead end-to-end deployment, operationalization, monitoring, and continuous improvement of AI/ML models in production. Establish MLOps automation and workflows, identify performance issues and data drift, lead incident response and root-cause analysis, and ensure reliable, scalable deployments. Partner with AI engineers, product teams, business stakeholders, risk, compliance, and governance teams to translate business needs into technical requirements, measure outcomes, and meet regulatory and ethical AI standards.

Designs, develops, tests, deploys, and operates AI-powered consumer support applications and services. Builds conversational experiences involving intent understanding, retrieval, response generation, orchestration, routing, and integrations. Develops evaluation and regression frameworks, improves reliability and performance, supports live-site operations, and uses telemetry and experimentation to address quality gaps. Collaborates with engineering, product, design, and support stakeholders while applying responsible AI, security, privacy, accessibility, and compliance practices.

Leads the development of reusable AI foundations, reference architectures, frameworks, and engineering practices across Development. Establishes approaches for agentic AI, LLMs, RAG, knowledge systems, simulation, digital twins, MLOps, LLMOps, and AgentOps. Guides technical teams, converts project solutions into scalable capabilities, promotes interoperability and reuse, and continuously evolves AI lifecycle standards and technical assets in partnership with reliability, product, governance, and technology teams.