AI Jobs in Dublin

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Design and ship production systems around models, owning integrations, data provenance, reliability, and on-call readiness across research, clinical, and operational workflows. Lead discovery and scoping from pre-sales through post-sales, translating ambiguous workflow needs into hypothesis-driven problem framing, system requirements, and an execution plan with measurable endpoints. Define and enforce launch criteria for regulated contexts, including validation evidence, audit readiness, outcome metrics, and drive delivery until sustained production impact is demonstrated. Build systems in sensitive scientific data environments where auditability, validation, and access controls shape architecture, operating procedures, and failure handling. Run evaluation loops that measure model and system quality against workflow-specific scientific benchmarks and use results to drive model and product changes. Distill deployment learnings into hardened primitives, reference architectures, validation templates, and benchmark harnesses that scale across regulated life sciences environments.

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The AI Safety Engineer is responsible for designing and building scalable backend infrastructure for content moderation, abuse detection, and agents guardrails by deploying AI/ML models into production systems. They will architect robust APIs, data pipelines, and service architectures to support real-time and batch moderation workflows. The role includes implementing comprehensive monitoring, alerting, and observability systems, establishing SLIs, SLOs, and performance benchmarks. The engineer will collaborate with ML engineers to translate research models into production-ready systems and integrate them across the product suite. Additionally, they will drive technical decisions and contribute to the vision for the safety roadmap to build next-generation platform guardrails for scale and precision.

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Design and evolve safety policies for audio AI, image/video AI and agentic safety aligned with ISO42001, EU AI Act, DSA, US state laws, and global regulatory developments. Build scalable, AI-powered systems and workflows that dramatically reduce response times and increase policy coverage. Partner with Safety Engineers to translate policy requirements into automated detection, moderation, and enforcement systems. Drive cross-functional safety integration with product, engineering, legal, and operations teams to ensure safety is embedded into the development lifecycle. Respond to safety policy escalations by partnering with moderation and investigations teams to triage, investigate, and resolve complex incidents, ensuring decisive and transparent action when user or platform integrity is at risk.

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The AI / ML Solutions Engineer at Anyscale is responsible for designing, implementing, and scaling machine learning and AI workloads using Ray and Anyscale directly with customers. This includes implementing production AI / ML workloads such as distributed model training, scalable inference and serving, and data preprocessing and feature pipelines. The role involves working hands-on with customer codebases to refactor or adapt existing workloads to Ray. The engineer advises customers on ML system architecture including application design for distributed execution, resource management and scaling strategies, and reliability, fault tolerance, and performance tuning. They guide customers through architectural and operational changes needed to adopt Ray and Anyscale effectively. Additionally, the engineer partners with customer MLE and MLOps teams to integrate Ray into existing platforms and workflows, supports CI/CD, monitoring, retraining, and operational best practices, and helps customers transition from experimentation to production-grade ML systems. They also enable customer teams through working sessions, design reviews, training delivery, and hands-on guidance, contribute feedback to product, engineering, and education teams, and help develop reference architectures, examples, and best practices based on real customer use cases.

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Lead development of advanced prompt engineering and retrieval pipelines by architecting and building scalable solutions that handle thousands of customer conversations daily across integrations such as Zendesk, Intercom, and Salesforce. Leverage generation pipelines to deliver rapid insights, accurate analysis, and meaningful customer interaction scoring. Architect real-time multimodal simulations to own the development of realistic, interactive training experiences across voice, video, and chat using platforms like OpenAI, ElevenLabs, and Vapi, ensuring simulations dynamically adapt to user input for immersive learning environments. Drive high-performance user experiences by setting technical standards for intuitive, lightning-fast interfaces built with Next.js, ensuring UIs handle complex AI interactions seamlessly under demanding workloads, and mentor others to achieve similar standards.

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Lead development of advanced prompt engineering and retrieval pipelines by architecting and building scalable solutions that handle thousands of customer conversations daily across integrations like Zendesk, Intercom, and Salesforce, leveraging cutting-edge generation pipelines for rapid insights, accurate analysis, and meaningful customer interaction scoring. Architect real-time multimodal simulations by owning the development of realistic, interactive training experiences across voice, video, and chat using platforms like OpenAI, ElevenLabs, and Vapi, with simulations dynamically adapting to user input to create immersive learning environments. Drive high-performance user experiences by setting technical standards for intuitive, fast interfaces built with Next.js, ensuring UIs handle complex AI interactions seamlessly under demanding workloads, and mentoring other engineers to achieve the same technical standards. Shape technical decisions, own major parts of the product, mentor teammates, and push personal skills in full-stack development including frontend, backend, and AI integrations, making critical decisions about architecture, tooling, and product direction.

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The AI Outcomes Manager will partner with executive sponsors and end users to identify high-impact use cases and turn them into measurable business outcomes on Glean. They will lead strategic reviews and advise customers on their AI roadmap to ensure maximum value from Glean's platform. The role involves translating business needs into clear problem statements, success metrics, and practical AI solutions while collaborating with Product and R&D to shape priorities. They will conduct discovery workshops, scope pilots, and guide rollouts to drive broad and deep adoption of the Glean platform. Additionally, they will design and build AI agents with and for customers, including rethinking and redesigning underlying business processes to maximize impact and usability. The manager will proactively identify expansion opportunities and drive engagement across teams and functions.

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The role involves training, evaluating, and monitoring new and improved LLMs and other algorithmic models. The engineer will test and deploy content moderation models in production and iterate based on real-world performance metrics and feedback loops. They are expected to develop medium to long-term vision for content understanding-related R&D, collaborating with management, product, policy & operations, and engineering teams. The position requires taking ownership of results delivered to customers, advocating for changes in approach where needed, and leading cross-functional execution.

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Design, develop, and deploy AI/ML models ranging from traditional ML regression algorithms to transformer-based architectures. Train, fine-tune, and optimize deep learning and LLM-based solutions. Engage with customers to understand their needs and transform them into actionable tasks for developing new functionalities within the Articul8 platform. Collaborate with researchers, software engineers, and product teams to integrate new AI capabilities into applications. Implement and evaluate state-of-the-art automated testing and metrics to improve model accuracy and efficiency. Optimize models for both cloud and on-premises environments to ensure low latency and high availability. Develop APIs and micro-services to serve AI models in production. Stay current with the latest AI models, research, and best practices. Ensure ethical AI practices, data privacy, and security compliance.

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Design and build robust RESTful and GraphQL APIs using Python, Go, or Rust. Architect scalable systems for real-time AI/ML-powered applications. Partner with ML engineers and frontend developers to ship intelligent, user-facing features. Ensure performance, security, and reliability across hybrid (cloud/on-prem) environments. Lead technical architecture and long-term backend strategy. Drive best practices for code quality, observability, and API design. Mentor engineers and help grow a high-performance, AI-augmented engineering culture. Take ownership of customer deployments and support production excellence.