Top AI & Machine Learning Jobs in Dublin

Lead engineering and deliver production-grade AI solutions using LLMs, RAG, and vector-search knowledge bases. Architect scalable cloud solutions (AWS, Cloudera, Databricks), build APIs and microservices, implement MCP-style orchestration, monitor model performance, ensure security and compliance, and mentor engineering teams.

Lead design and deliver enterprise AI solutions using LLMs, RAG, and vector search. Build knowledge bases, LLM-driven apps (LangChain), secure cloud/databricks integrations, APIs/microservices, monitor model performance, and mentor engineers while ensuring compliance and operational excellence.

Design and deliver production-grade AI solutions using LLMs, RAG, and vector-search knowledge bases. Build scalable cloud-native APIs and microservices, integrate LLM frameworks (e.g., LangChain/MCP), ensure security and compliance, monitor model performance, and mentor engineers.

Lead the design and delivery of enterprise-grade AI solutions using LLMs, RAG, and vector-search knowledge bases. Build scalable, secure cloud-native systems (AWS, Cloudera, Databricks), develop APIs and microservices, implement LLM frameworks (e.g., LangChain), optimize models in production, and collaborate across product, engineering, and data teams while ensuring governance, security, and compliance.

Lead design and deliver enterprise AI solutions using LLMs, RAG, and vector-search knowledge bases. Build scalable cloud architectures (AWS, Cloudera, Databricks), APIs and microservices, integrate LLM frameworks (e.g., LangChain), monitor model performance, and ensure security/compliance. Mentor engineers, drive technical design, and collaborate with product and data teams to productionize AI capabilities.

Lead engineering teams to build and operate cloud-native API platforms exposing foundation model capabilities. Drive API and distributed systems design, security, scalability, observability, CI/CD, and model integration while managing delivery, hiring, and stakeholder alignment.

Lead engineering strategy and delivery for agentic AI products and platforms. Build and scale teams, drive architecture for production-grade agent systems, establish reusable frameworks, partner cross-functionally, and ensure security, observability, and governed AI adoption.

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 strategy and hands-on delivery for agentic AI products and reusable agent frameworks. Build and scale teams and cloud-native platforms, drive architecture for production-grade agent systems (memory, orchestration, tool integration, observability, safety), partner cross-functionally, establish engineering frameworks, and measure adoption, quality, and business impact.

Lead engineering for agentic AI products and platforms: build and scale teams, define architecture and frameworks for LLM-driven agents, ensure production-grade reliability, observability, security, and cross-functional adoption of AI tooling and governance.