Top AI Jobs in Dublin

Lead development and scaling of AI-powered clinical applications using LLMs. Gather user and industry requirements, write product specifications, prioritize roadmaps, coordinate sprints and releases, validate quality with engineering and QA, and design generative AI capabilities to streamline pharmaceutical workflows and regulatory documentation.

Lead product marketing and go-to-market for Sia's AI product portfolio. Develop positioning, messaging, launch plans, field enablement assets (playbooks, demos, pitch materials), and coordinate cross-functional campaigns, events, and partner activity to drive adoption and awareness among technical and business audiences.

Build end-to-end AI-native product features across frontend, backend, and model integrations. Design and deploy agent workflows, integrate LLMs, memory, and external tools, and ensure real-time streaming, low-latency interactions, reliability, observability, and robust fallback/recovery. Collaborate with ML, backend, and product teams and iterate based on real-world usage.

Build and ship end-to-end AI features, shaping model behavior and surrounding systems. Design prompts, tools, memory, and agent workflows; convert model outputs into reliable behavior; debug across model, infra, orchestration, and UX; optimize latency, cost, and reliability; and develop evaluation frameworks to measure real-world performance.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Partner with Account Executives to present Arize's GenAI observability platform, lead technical demos and discovery, design and run PoCs, advise on GenAI/LLM best practices, handle technical objections, and build relationships with technical and business stakeholders across prospective customers.

Partner with customers to design, prototype, and deploy AI-enabled cybersecurity workflows using OpenAI models and tooling. Lead discovery, scope pilots, build demos/proofs, advise on safe implementation patterns, create reusable field assets, and provide product feedback across AppSec, cloud security, SOC/IR, vulnerability management, and related domains.

Lead end-to-end architecture for enterprise data, analytics, and AI in a Microsoft-first environment. Translate business needs into secure, scalable designs, prototype and guide delivery teams, ensure governance, security, and production readiness, and enable enterprise AI (Azure OpenAI, RAG) and BI (Power BI) solutions.

This role involves improving AI systems' reliability, designing monitoring systems, managing failover systems, and leading incident responses for critical AI services.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Develop and optimize large neural-network tabular models in Python. Profile training and inference pipelines, improve memory efficiency, latency, and throughput. Implement performance-critical components in Rust (and optionally C++), ensure numerical stability and reproducibility, collaborate with researchers to productionize capabilities, and maintain tests and documentation.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

Lead and build Maze’s new AI-native Market Research service: define research vision and methodologies, recruit and manage a research team, own delivery quality, partner with Product/Engineering and GTM, and serve as executive research lead on strategic customer engagements.

The Director of Business Development will drive revenue through creating strategies for new business, managing client relationships, and overseeing sales processes while ensuring high service standards.

Design, build, and maintain ELT pipelines and dbt SQL models for analytics; optimize performance, scalability, and costs; apply AI-enabled tools to improve engineering workflows; collaborate to translate requirements into production-grade data assets.

As an AI Creative Producer, you'll develop and scale AI-generated content across various platforms, collaborate with teams, and enhance production workflows.

Design, build, and deploy production-grade agentic AI systems: multi-agent orchestration, RAG pipelines, provider abstraction, LLMOps (eval harnesses, prompt versioning, observability), cloud-native deployment (Kubernetes/Docker/CI-CD/IaC), and embed with client teams to deliver reusable patterns and measurable metrics for accuracy, latency, safety, and cost.

Design, develop, test, and ship AI-powered features and agent/LLM workflows for large datasets. Rapidly prototype, iterate with users, integrate agentic components with internal tools, and ensure scalable, production-grade AI solutions using cloud-native and observability practices.

Build and productize AI-native systems for a fintech neobank: AI agents, RAG/LLM-powered workflows, tool integrations, and automations for onboarding, KYC, support, risk, CRM and operations. Focus on production reliability, evaluation, guardrails, latency, cost, security and compliance while collaborating with product and engineering teams.

The Business Development Rep will qualify clients and manage outreach in AI Cloud sales, engaging technical audiences and developing partnerships.

Lead strategy and execution for AI-powered asynchronous engagement across messaging, email, and proactive/predictive journeys. Define vision, measurable outcomes, and portfolio priorities; partner with AI, engineering, and go-to-market teams; engage customers; build and coach a product organization to operationalize AI at enterprise scale and deliver measurable automation and CX improvements.

Remote, part-time role testing an AI tool in Irish: perform guided tasks, report results, follow onboarding and troubleshooting, use MS Teams and basic apps, ~1–3 hours/day up to 15 hrs/week for at least two months, paid $20–$35/hr.

Lead and build Axelera's AI Infrastructure Systems division to deliver datacenter-class hardware from accelerator cards and OAM/UBB modules to 1U/2U racks. Own architecture, schematics, PCB/layout review, power/thermal/mechanical design, BMC/firmware, bring-up, validation, qualification, NPI, and ramp with CMs/ODMs. Align roadmaps with silicon, SDK, and product teams, engage customers and partners, set engineering processes, manage budget and vendor relationships, and mitigate technical risks through hands-on leadership.

Lead design, training, and evaluation of LLMs for translation and language-quality tasks; build robust evaluation frameworks; architect agentic multi-model workflows; own model development from data curation to production; run reproducible experiments; collaborate with product and engineering and mentor researchers.