Top AI Jobs in Dublin

Own AI Investing product areas across financial services, defining user problems, scope, metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify complex financial journeys. Use research, feedback, metrics, experiments, and production issues to guide decisions, launch features, monitor performance, and drive continuous improvement while balancing speed, user experience, business impact, compliance, and operational complexity.

As a Solutions Engineer in the Technical Success team, you will guide Digital Native customers in leveraging OpenAI's APIs to maximize business value, manage pre-sales needs, and ensure effective deployment of AI solutions across various applications.

Design, build, and deploy production-grade agentic AI systems at enterprise scale. Lead client engineering engagements, define RAG and multi-LLM standards, own LLMOps (eval, prompt governance, observability), implement cloud-native architectures, and publish reusable patterns. Manage and develop engineering teams and measure agentic system quality across accuracy, latency, safety, and cost.

Own AI Finance product areas across insurance, payments, savings, investing, travel, and other financial services. Define user problems, roadmaps, metrics, scope, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial journeys; and use data, research, feedback, and production insights to drive iterations and improvements.

Own product areas for BJAK’s AI Stockbroking business, defining user problems, product scope, metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to deliver financial products. Use research, feedback, experiments, metrics, and production insights to improve customer journeys and product performance while balancing speed, usability, business impact, compliance, and operational complexity.

Own AI Investing product areas across financial services, defining user problems, product direction, scope, metrics, roadmaps, and launch plans. Partner with engineering, design, data, operations, compliance, and business teams to simplify complex financial products. Launch features, monitor performance, use research and feedback to guide iterations, and balance speed, user experience, business impact, compliance, and operational complexity.

Own product areas within an AI neobank across financial services such as insurance, payments, savings, investing, and travel. Define user problems, product direction, scope, metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to deliver customer journeys, launch features, monitor performance, and iterate using data, feedback, research, and production insights. Balance speed, user experience, business impact, compliance, and operational complexity.

Own product areas across financial services, including insurance, payments, savings, investing, and travel. Define user problems, product direction, scope, metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify complex workflows, ship features quickly, monitor performance, and drive iterative improvements based on data, feedback, research, and production issues.

Own AI Finance product areas across insurance, payments, savings, investing, travel, and related financial services. Define user problems, roadmaps, success metrics, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial workflows; use data and feedback to guide decisions; launch features, monitor performance, and drive continuous iteration while balancing speed, customer experience, business impact, compliance, and operational complexity.

Own product areas within BJAK’s AI Neobank across financial services including insurance, payments, savings, investing, and travel. Define user problems, roadmaps, metrics, scope, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial workflows; launch features; monitor performance; and iterate using data, research, feedback, and production insights.

Own product areas across financial services, including insurance, payments, savings, investing, and travel. Define user problems, product scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial workflows; launch and monitor features; and iterate using data, feedback, research, and production insights.

Own product areas across AI Finance, including insurance, payments, savings, investing, and travel. Define user problems, product direction, scope, metrics, roadmaps, and launches. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify financial products, ship features, monitor performance, and iterate using data, research, feedback, and production insights.

Leads the identification, development, testing, and scaling of AI and automation solutions across internal delivery processes and future customer-facing use cases. Builds automated test suites, scripts repetitive workflows, pilots generative AI and agentic tools, creates reusable automation assets, tracks measurable value, and enables teams through training and knowledge sharing. Collaborates with project managers, product owners, process owners, testing communities, and delivery teams to ensure maintainable, responsible, and broadly adopted solutions.

Lead Vertical AI transformation and optimize marketing sites. Oversee AI tools, manage projects, establish best practices, and enhance site performance.

Lead AI-driven resilience assurance initiatives, utilizing predictive analytics and real-time monitoring to enhance compliance and organizational resilience. Develop autonomous systems for continuous audits and dynamic maturity assessments, translating insights into actionable strategies for stakeholders.

Lead and mentor a team of Applied Scientists and AI/ML Engineers to design, build, and deploy production-grade AI features for an AI-native CRM. Drive technical strategy for LLM operations, agentic workflows, RAG/vector-based solutions, and scalable data pipelines while partnering with platform teams to ensure performant, customer-centric ML systems.

Lead Moonshot's applied AI safety portfolio, directing red teaming, adversarial evaluation, and methodological standards across online harms. Advise AI companies and regulators, manage client relationships and staff, develop offerings and funding, and ensure ethical, documented delivery of safety evaluations and interventions for violence, CSEA, mental-health, and child/teen risks.

The Backend Engineer will develop and maintain features for Restream web services, design backend APIs, ensure infrastructure resilience, and contribute to a collaborative team environment.

Design and implement AI-powered internal tools for GTM use cases, managing full lifecycle from data modeling to production. Collaborate closely with GTM teams to translate data into actionable insights with a focus on measurable business impact.

Manage multilingual data collection and Large Language Model evaluation projects, ensuring quality assurance and effective global team coordination.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Build and maintain the marketing automation backbone and AI agent integrations. Implement event tracking, data pipelines, CRM customizations, and cross-channel workflows. Prototype agent infrastructure, connect APIs/webhooks, automate go-to-market tasks, and produce analytics and attribution to support growth and experimentation.

Design and implement user-facing features from concept through implementation, combining UX design and front-end engineering. Build, iterate, and maintain UI/UX and business logic to drive engagement and conversion, collaborate with product/marketing/support, participate in code reviews, and leverage modern tools and deployment workflows.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

Develop and maintain open-source, silicon-optimized AI/ML inference application packages (Inference Snaps) for Linux/Ubuntu Core. Debug, write high-quality code, review upstream contributions, secure and optimize local inference deployments across embedded devices to gateways, and collaborate with a distributed team and stakeholders.