Top AI & Machine Learning Jobs in Dublin

Design and deliver enterprise-grade AI solutions using LLMs, RAG, and vector search. Build knowledge bases, APIs, and microservices; deploy scalable, secure systems on cloud platforms (AWS, Cloudera, Databricks). Collaborate cross-functionally, monitor model performance, ensure data governance and compliance, and mentor junior engineers.

Lead engineering teams to build and operate cloud-native API platforms exposing foundation model capabilities. Own architecture, security, reliability, CI/CD, observability, model integration, and delivery across multiple workstreams while hiring, coaching, and partnering with stakeholders to translate product needs into technical roadmaps.

Lead engineering team building and operating cloud-native API platforms exposing foundation model capabilities. Define API and service architecture, enforce security and reliability, drive CI/CD and observability, integrate models with production systems, manage multiple workstreams, and hire/coach engineers to deliver secure, scalable, production-grade AI platform services.

Lead design and delivery of enterprise-grade AI solutions using LLMs, RAG, and vector search. Build knowledge bases, LLM-driven applications (LangChain), and scalable cloud architectures (AWS, Cloudera, Databricks). Implement secure microservices/APIs, monitor model performance in production, mentor engineers, and ensure compliance with data governance and security standards.

Lead design and deliver enterprise AI solutions using LLMs, RAG, and vector search. Build knowledge bases, APIs, and microservices; architect scalable cloud/data platforms (AWS, Cloudera, Databricks); implement LLM orchestration (LangChain, MCP); monitor and optimize production model performance; ensure data governance, security, and compliance; mentor engineers and contribute to technical design.

Lead design and deliver enterprise AI solutions using LLMs, RAG, and vector-search knowledge bases. Architect secure, scalable cloud/data systems (AWS, Cloudera, Databricks), build APIs and microservices, implement LangChain-based apps, optimize production model performance, and mentor engineers while ensuring data governance and regulatory compliance.

Lead engineering strategy and hands-on delivery for agentic AI and LLM-powered products. Build and scale platforms, reusable agent frameworks, and orchestration for production-grade agents. Partner with Product, Data Science, Security, and Platform teams to translate use cases into secure, observable, and reliable systems. Drive AI tooling adoption, engineering standards, lifecycle management, and developer experience while measuring adoption, quality, latency, resilience, and business impact.

Lead an engineering team building API platforms and backend services to expose foundation model capabilities. Own architecture, security, reliability, CI/CD, observability, and model integration while managing delivery, hiring, and stakeholder alignment for production-grade AI services.

Lead and scale engineering teams to design, build, and productionize agentic AI systems and reusable platform capabilities. Define architecture, frameworks, observability, safety guardrails, and developer tooling to move LLM/agent solutions from experimentation to secure, scalable enterprise production while partnering with product, data science, security, and platform teams.

Design and deliver enterprise AI solutions using LLMs and RAG; build and maintain vector-search knowledge bases; develop LangChain-driven applications; architect secure, scalable cloud/data systems (AWS, Cloudera, Databricks); implement APIs and microservices; monitor model performance; mentor engineers and ensure data governance and security compliance.