Top Tech Jobs & Startup 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-grade AI solutions using LLMs, RAG, LangChain and vector search. Build and maintain knowledge bases, architect scalable cloud solutions (AWS, Cloudera, Databricks), develop APIs/microservices, ensure security/compliance, monitor model performance, mentor engineers, and collaborate cross-functionally to translate business needs into production AI systems.

Design and deliver enterprise AI solutions using LLMs, RAG, and vector search. Build knowledge bases, scalable cloud-native services (AWS/Cloudera/Databricks), APIs/microservices, and production-grade model integrations. Monitor model performance, ensure security and compliance, mentor engineers, and collaborate cross-functionally to translate business needs into AI-enabled products.

Design, build, and deliver enterprise-grade AI solutions using LLMs, RAG, LangChain, and vector search. Architect secure, scalable cloud/data systems (AWS, Cloudera, Databricks), develop APIs and microservices, optimize model performance in production, and mentor engineers while ensuring governance, security, and compliance.

Design, develop, and deploy enterprise-grade AI solutions using LLMs, RAG, and vector-search knowledge bases. Build scalable, secure cloud-native systems (AWS, Cloudera, Databricks), APIs, and microservices; implement LangChain and MCP patterns; monitor and optimize production model performance; ensure data governance and compliance; mentor engineers and drive engineering best practices.

Lead design and deliver enterprise AI solutions using LLMs, RAG, and vector search. Build knowledge bases, APIs, and scalable cloud-native systems (AWS, Cloudera, Databricks). Mentor engineers, ensure security/compliance, optimize model performance, and collaborate cross-functionally to productionize AI applications.

Lead security advocate for Data Commercialization and AI programs. Advise product and engineering teams on secure design, translate security policies into actionable guidance, lead security reviews and governance (threat modeling, code/design reviews, 3rd-party assessments), embed security in SDLC and CI/CD, and drive protection of sensitive data and ML models through automation and standards compliance.

Lead engineering strategy and hands-on delivery for agentic AI products and platforms. Build and scale teams and reusable agent frameworks, drive architecture for production-grade LLM/agent systems (context, memory, tool integration, orchestration, observability, safety), partner with Product and Data Science, and establish engineering standards, tooling, and measurable success criteria to move experiments into governed, scalable production.

Lead and deliver production-grade agentic AI systems and platforms. Define engineering vision, build and scale teams, architect agent frameworks and orchestration, ensure security, observability, and governance, partner with Product and Data Science, and drive adoption of AI tooling and developer productivity across the organization.

Lead and build a Support & Retention Experience function to monitor customer health, establish post-production support and SLAs, automate playbooks, integrate cross-functional data, identify expansion opportunities, and provide leadership visibility to improve retention and platform adoption.